57 results about "Non-negative matrix factorization" patented technology

The invention discloses an intelligent multi-gas detection module data processing system and method based on NDIR, and relates to the technical field of gas detection.The method comprises the steps that a multi-wavelength NDIR sensor is used for obtaining light intensity changes of a gas sample under different wavelengths, and original spectral signals are generated; performing denoising processing on the spectral signal by adopting wavelet transform, and performing zero calibration and dynamic baseline deduction; based on the Beer-Lambert law in combination with environmental factors, establishing a relation model between gas absorption and spectral signals; updating model parameters by using a recursive least square algorithm, and continuously optimizing gas concentration prediction; separating gas signals by adopting a non-negative matrix factorization algorithm, and predicting the concentration of each gas; and the edge end operates the lightweight model in real time, and regularly uploads the model to the cloud end for global optimization and federated learning. The method can effectively improve the condition that the precision is insufficient when the model is used for a long time in the prior art.

The invention relates to the technical field of digital audio processing, in particular to a digital audio processing method and system for sound equipment. The system comprises an audio signal acquisition module, a signal analysis module, an adaptive adjustment module, a howling suppression module and a sound effect optimization feedback module. The audio signal acquisition module acquires data such as frequency spectrum and amplitude of the sound equipment in real time. And the signal analysis module generates a signal analysis report based on a non-negative matrix factorization algorithm and a generative adversarial network algorithm. And the adaptive adjustment module automatically adjusts audio parameters through a fuzzy logic control algorithm. The howling suppression module suppresses howling noise by using an adaptive feedback cancellation algorithm. And the sound effect optimization feedback module adopts a Bezier curve to enhance the sound effect and realize frequency, phase and space positioning optimization. Through cooperative processing of audio acquisition, accurate analysis, adaptive adjustment, howling suppression and spatial sound effect optimization, the audio quality and stability are improved, and the audio spatial effect and user experience are enhanced.

The invention relates to the technical field of building energy saving, and discloses a building energy-saving control system and method.The method comprises the steps that firstly, multi-source environment data inside and outside a building is collected, and a time-space fusion data set is generated through timestamp synchronization and data alignment; dynamic energy consumption characteristics are extracted, and the future energy consumption state of each region is predicted by using a hidden Markov model; decomposing a historical energy consumption mode according to a prediction result and a non-negative matrix factorization algorithm to generate a multi-objective optimization control strategy; and finally, adjusting an equipment operation sequence through a dynamic priority scheduling algorithm and outputting an instruction. The system comprises a multi-source data acquisition module, a feature extraction module, a state prediction module, a strategy generation module, an equipment scheduling module and a feedback correction module. According to the method, energy consumption can be accurately predicted, multi-target optimization control, dynamic equipment scheduling and online strategy correction are realized, the building energy utilization efficiency is effectively improved, energy consumption is reduced, the comfort level is guaranteed, and equipment loss is reduced.

The invention belongs to the field of ionosphere satellite magnetic field seismic anomaly extraction, and particularly relates to a satellite magnetic field seismic anomaly extraction method based on complex non-negative matrix factorization, which comprises the steps of reading Swarm A satellite magnetic field data, and removing a main magnetic field of a ground core and a lithosphere magnetic field of an earth crust to obtain a residual magnetic field; performing short-time Fourier transform on the preprocessed data to obtain a complex time-frequency matrix in a rectangular coordinate representation form of a complex domain; decomposing the complex time-frequency matrix by using complex non-negative matrix decomposition to obtain R feature components; performing inverse short-time Fourier transform on each feature component to obtain a time domain feature component; a mean square root of a time domain seismic component of each orbit is calculated, a threshold value is set by using an over-limit rate method, and abnormal points are extracted from the seismic components of the orbits. The amplitude and phase information contained in the time-frequency domain of the satellite observation data is fully utilized, so that the component related to the earthquake is identified, and the earthquake anomaly extraction is more accurate and reliable.

The invention discloses a wireless audio star flash transmission noise management method and system suitable for a conference system, and the method comprises the steps: obtaining to-be-transmitted audio information, coding the to-be-transmitted audio, carrying out the preliminary separation of the to-be-transmitted audio based on a non-negative matrix factorization algorithm, and obtaining the separated audio information; performing audio classification according to the separated audio information, and classifying to-be-transmitted audios into different audio types to obtain audio classification information; acquiring environment sound audio information according to the audio classification information, forming an environment sound audio sequence, and performing noise reduction management; human voice audio analysis is carried out, the human voice audio is divided into background human voice audio and target human voice audio, noise reduction is carried out on the background human voice audio, and anomaly detection is carried out on the target human voice audio; and carrying out comfort evaluation on the target human voice audio, judging whether human noise exists or not, carrying out audio prediction, and generating noise reduction regulation and control parameters. And the audio transmission noise management quality of the conference scene and the comfort of participants are improved.

The invention provides an acupoint compatibility prediction method and device, equipment and a medium, and relates to the technical field of traditional Chinese medicine acupoint compatibility prediction.The method comprises the steps that acupoint compatibility information and acupoint attribute information of to-be-predicted acupoint compatibility are obtained, and an acupoint feature composite graph is constructed based on the acupoint compatibility information and the acupoint attribute information; constructing a de-noised acupoint feature matrix on the basis of a deep non-negative matrix factorization method according to the acupoint feature composite graph, training the de-noised acupoint feature matrix through a residual image attention network model to obtain a target acupoint feature matrix, and obtaining an association relationship of compatibility of the to-be-predicted acupoints according to the target acupoint feature matrix so as to predict the compatibility of the to-be-predicted acupoints. A deep non-negative matrix factorization method is used for carrying out layer-by-layer factorization and fine adjustment on an adjacent matrix of the acupoint feature composite graph to construct a de-noised acupoint feature matrix, random noise in the network can be relieved, comprehensive and effective acupoint feature representation is obtained through a residual image attention network model, and the prediction accuracy is effectively improved.

The invention discloses a cross-multi-camera multi-target tracking method, system, device, medium and product, and relates to the field of computer vision, and the method comprises the steps: obtaining a video image, employing a single-camera multi-target tracking architecture according to the video image, and obtaining a local track of single-camera multi-target tracking, the single-camera multi-target tracking architecture comprises a detector, a ReID module and a tracker; according to all single-camera multi-target tracking local tracks, a local track set is obtained, the target appearance features, the target detection frames and the target positions of every two local tracks and the topological information of the corresponding cameras are utilized to determine the track similarity measurement of the two local tracks, and then a matching method of limited non-negative matrix factorization is adopted to determine the target similarity of the two local tracks. Obtaining a global trajectory of the target; according to the invention, a high-precision tracking task of a target in a complex scene can be realized.

The invention provides a face image clustering method and device, equipment and a storage medium. Relates to the technical field of image processing. The method comprises the following steps: inputting required clustering data, wherein the clustering data is a high-order graph regular non-negative matrix; calculating and constructing a high-order similarity matrix; constructing a high-order graph constraint; defining and initializing each layer of basis matrix and representation matrix of regular non-negative matrix factorization of the high-order map; obtaining an updating formula of each layer of basis matrix and an expression matrix of the regular non-negative matrix factorization of the high-order map; updating the basis matrix and the representation matrix of each layer of the regular non-negative matrix factorization of the high-order map; obtaining a low-dimensional representation matrix; and outputting a clustering result. According to the invention, for the face image data with complex similarity, similarity information of more levels can be captured, so that the clustering performance is improved.

The invention discloses a multi-view learning method and device based on low-rank constraint and hypergraph learning and a medium, and relates to the technical field of multi-view clustering. The method comprises the following steps: performing depth non-negative matrix factorization on a data matrix of multiple views to obtain a coefficient matrix; constructing a reconstruction error of depth non-negative matrix factorization through a norm; performing low-rank constraint on the coefficient matrix to obtain a low-rank constraint term; performing hypergraph structure learning on the coefficient matrix to obtain a hypergraph regular term; establishing a multi-view learning objective function based on the reconstruction error, the low-rank constraint term and the hypergraph regular term; and solving the objective function to obtain a learning result of the multiple views. Low-rank constraint is used for a coefficient matrix, sparsity of the coefficient matrix is enhanced, redundant information of the coefficient matrix is removed, a hypergraph structure is learned while the coefficient matrix is constructed, multivariate relations between data points are learned, manifold structure information of a source space is reserved, expression of the coefficient matrix on data deep information is enhanced, and then accuracy of a clustering result is improved.

A method for operating a driver monitoring system for a vehicle, comprising the following steps: Receiving a plurality of gaze target points for a driver of the vehicle; inputting the plurality of gaze target points into a predictive distraction probability model to obtain a predictive distraction distribution; Determine whether one or more meaningful gaze locations exist in the plurality of gaze target points; Comparing the predictive distraction distribution to a predictive distraction distribution threshold if one or more meaningful gaze locations are present in the plurality of gaze target points; and Warn the driver when the predictive distraction distribution reaches or exceeds the predictive distraction distribution threshold; wherein the predictive distraction probability model includes developing a graphical representation of the plurality of gaze target points; wherein the graphical representation is a histogram graph of the plurality of gaze target points; wherein the graphical representation is used to develop a gaze gray-level matrix that captures a plurality of gaze transitions with the plurality of gaze target points; wherein the method further comprises the step of factoring the gaze gray value matrix using a non-negative matrix factorization (NMF) technique to develop a plurality of NMF factors.

The invention provides a wheat leaf area index whole growth period estimation method based on canopy pure hyperspectral reflectivity, which comprises the following steps of: firstly, extracting wheat canopy spike, leaf and soil pure pixels based on an RGB (Red, Green and Blue) image combined with a deep learning algorithm; secondly, separating the canopy pure leaf hyperspectral reflectivity by using the extracted pure pixels and canopy hyperspectral reflectivity data in combination with a non-negative matrix factorization algorithm; and finally, calculating a vegetation index based on the canopy pure leaf hyperspectral reflectivity, and establishing a whole-growth-period LAI estimation model in combination with a simple linear regression algorithm. According to the method, the problem that the LAI inversion precision is reduced after heading is solved by combining near-end remote sensing and a machine learning algorithm, and the wheat LAI estimation method based on the canopy pure hyperspectral reflectivity has the potential of accurately predicting the LAI of the wheat in the whole growth period and can be expanded to accurate management of field crops and breeding of high-yield wheat varieties.

This application discloses a deconvolution method for gas chromatography-mass spectrometry (GC-MS) data, relating to the field of GC-MS data analysis technology. This method utilizes a non-negative matrix factorization function to initially decompose the time-mass-charge ratio two-dimensional intensity matrix, obtaining an initial elution curve matrix and an initial mass spectrometry matrix. An exponentially modified Gaussian peak shape modeling function is then used to fit the initial peak shape parameter set. For each component: based on the initial peak shape parameter set, an exponentially modified Gaussian peak shape modeling function is used to generate the original elution curve matrix. Non-negative least squares optimization and non-linear least squares optimization methods are used to iteratively optimize the mass spectrometry matrix, peak shape parameter set, and elution curve matrix. Finally, a comprehensive scoring function is used to select the optimal component set, and the optimal peak shape parameter set, mass spectrometry matrix, and elution curve matrix for the optimal component set are sorted and output. This application enables precise separation of co-eluted components, improving the accuracy and reliability of complex mixture analysis.

The application proposes an unsupervised explanation method based on adaptive semantic features, and the constructed explainable model mainly includes three core modules: feature-level explainability module, adaptive feature expression module and feature importance calculation module. Firstly, the deep feature mapping of the deep neural network is regarded as the abstract expression of the high-level concept semantics learned by it, then the non-negative matrix decomposition technology is used to unsupervisedly extract key semantic information from it, and feature-level explanation or structured attribution is carried out; through the dimension scaling scheme, adaptive number of feature extraction is realized; in the feature importance calculation, the Shapley value algorithm is used for calculation. In addition, a saliency visual explanation is generated to highlight the key area of the model decision. Experiments show that the method has higher explanation accuracy, and in the environment of different data sets and explained models, the explanation accuracy is better than that of the existing method, and has the characteristics of accurate explanation, robustness and universality.

The invention relates to the technical field of immunohistochemical staining image quantitative analysis, in particular to an immunohistochemical staining image quantitative analysis method and system, and the method comprises the following steps: S1, obtaining a digital image of an immunohistochemical staining section; and S2, carrying out color deconvolution processing on the digital image, and decomposing the digital image into an exclusive channel image corresponding to at least one target coloring agent and a background channel image. According to the method, color deconvolution is realized through a non-negative matrix factorization algorithm, target coloring agent signals and background and other dyeing signals in the composite dyeing image are accurately split, and the problem that dyeing components of an existing system are not thoroughly separated is solved; through combination of a Mask R-CNN deep learning network and multi-mode adaptive parameters, positive and negative cells in different dyeing modes can be accurately identified, and multiple scenes such as nuclear dyeing, membrane dyeing and slurry dyeing can be adapted.

The invention relates to the field of machine learning and pattern recognition, and particularly discloses a variance-covariance subspace distance-based multi-kernel subspace clustering method, which comprises the following steps of: S1, acquiring a high-dimensional data matrix, calculating a variance-covariance matrix and second-order statistical information thereof, defining a preliminary VCSD kernel value and obtaining a VCSD kernel matrix through exponential transformation and normalization, constructing a multi-core pool of r base cores; s2, constructing an optimized objective function based on non-negative matrix factorization, and solving to obtain a local affinity feature map of a block diagonal structure under constraint conditions; s3, capturing high-order feature association through a matrix method, and converting tensor optimization into matrix operation; s4, solving the target function by adopting an alternating optimization strategy, reducing the calculation complexity, and generating a target affinity graph; and S5, clustering the target affinity spectrum, and outputting a result. According to the technical scheme provided by the invention, the problems of missing statistical characteristics, complex calculation and nuclear noise interference of the existing method are solved, and the clustering precision and efficiency are effectively improved.

The application belongs to the field of satellite data processing, and particularly relates to a Swarm satellite electron density data daily global drift disturbance automatic identification and removal method, which comprises normalization processing; short-time Fourier transform is performed on the normalized data to obtain a time-frequency matrix, the time-frequency matrix is classified into strong disturbance orbit data and other orbit data by using a two-dimensional convolutional neural network, and a component containing daily global drift disturbance is identified; for the decomposition result of the strong disturbance orbit, the identified disturbance component is removed; for the disturbance component of the other orbit, a medium-strong disturbance interval is identified and located, and a disturbance latitude range is obtained according to a mapping relationship between an image coordinate and a latitude; for the identified medium-strong disturbance orbit, pre-normalization data thereof are obtained and subjected to non-negative matrix decomposition, a corresponding part in the disturbance component is cut off according to the disturbance latitude range, and the remaining part and the remaining components are added to serve as effective signals and reserved, so that automatic cleaning of massive satellite data is realized.

The invention relates to the field of machine learning, provides an artificial intelligence-based occupational hazard risk assessment system, and aims to solve the problems of serious missing of multi-dimensional risk data, insufficient feature expression and low assessment precision. The system is composed of a data acquisition module, a data enhancement module and a risk assessment module. Unified tensorization processing is carried out on the data through a segmented weighted non-negative matrix factorization model, an error function of L1 and L2 norm combination is introduced, and the robustness and accuracy of data completion are improved; on this basis, constructing a Bargmann-SVM model, mapping standardized data to a Bargmann space, obtaining complex high-dimensional features by extracting a high-order derivative and a projection coefficient of a full-pure function, training a support vector machine in combination with a polynomial radial basis kernel, and performing accurate classification and grade evaluation; the system can be widely applied to high-risk environments such as factories and workshops, and the intelligent recognition and early warning capability of occupational hazard risks is improved.

The invention discloses a non-negative matrix factorization face clustering method and device based on graph learning and pseudo tag guidance and a medium, and relates to the technical field of data analysis and machine learning. The method comprises the following steps: acquiring a data matrix, a pseudo tag matrix, a regularization parameter and a clustering number; the data matrix comprises face image data; randomly initializing a basis matrix, a coding matrix, a self-expression coefficient matrix and an auxiliary matrix; determining an affinity matrix and a diagonal matrix, and determining a graph Laplacian matrix according to the affinity matrix and the diagonal matrix; and carrying out iteration based on the set maximum number of iterations, and in each iteration process, sequentially updating each matrix and minimizing the target function until the target function converges or the number of iterations reaches the maximum number of iterations, so as to obtain a clustering result. According to the method, by combining a self-representation method in subspace learning, the representation coefficient is generated, the Laplacian matrix is further calculated, and the Laplacian matrix is updated in the iteration process, so that generation of the optimal solution is guaranteed.

The invention discloses a multi-view clustering method for adaptively fusing similar graphs, which comprises the following steps of: performing non-negative matrix factorization on an original data matrix, and introducing the constraint of the similarity of data points on a non-negative embedded matrix by utilizing the consistency and complementarity of each view of data to obtain a common matrix capable of directly outputting a clustering result; and the clustering result is evaluated by using four evaluation indexes, namely accuracy, an adjustment Lam coefficient, a purity index and normalized mutual information. On the basis of non-negative matrix factorization, the weight of each view is adaptively adjusted according to the contribution of the common view, the difficulty of hyper-parameter determination is avoided, meanwhile, the constraint of the similarity graph on the non-negative matrix is introduced, and the non-negative matrix factorization of the common regularization consistent constraint view based on the center is utilized, so that the non-negative matrix factorization is realized. And finally, a common matrix containing clustering result information is obtained, and the clustering performance is greatly improved.

The invention relates to a model-driven deep learning multicolor fluorescence blind source unmixing method, which comprises the following steps of: establishing an optimization problem of regularization constraint containing image denoising based on a linear spectrum mixing model, and constructing a non-negative matrix factorization blind source unmixing framework; non-negative matrix factorization is realized based on gradient descent and an iterative shrinkage threshold algorithm, the gradient descent and the iterative shrinkage threshold algorithm are expanded into a multi-layer recurrent neural network, and a transformation function is replaced by the multi-layer convolutional neural network; designing a spectrum extraction algorithm to obtain prior information of a fluorescence emission spectrum, and initializing parameters of the multilayer recurrent neural network according to the prior information; and constructing a multicolor fluorescence imaging simulation data set, and carrying out end-to-end training and testing on the multilayer recurrent neural network. Compared with a method for demixing the multicolor fluorescence image directly through a traditional non-negative matrix factorization algorithm, the method has the advantages that a better demixing effect is obtained under the condition that a small number of network layers is used, artifacts are fewer, and structural information is more accurate.

The invention relates to a community detection method and device and a storage medium, and the method comprises the steps: determining the harmonic centrality of each user in a communication user network, constructing community division according to the harmonic centrality and a preset community number estimation range, and obtaining a plurality of candidate communities; calculating the affiliation probability and the community modularity of nodes in each candidate community so as to determine the optimal community number; selecting a target community center node according to the optimal community number and the harmonic centrality of each user; constructing a node similarity matrix according to the target community center node, and constructing an optimization target function according to the node similarity matrix; and performing iterative solution on the target function by adopting a non-negative matrix factorization iterative optimization algorithm, and determining a community division result according to a solution result. The problem that the number of communities needs to be preset in a traditional method is effectively solved, and the method has good adaptivity and stability.

The present invention belongs to the field of image classification technology, and discloses a clustering method, device and medium of graph regular non-negative matrix decomposition, the method comprising: obtaining matrix information data and the number of to-be-clustered; defining four initialization sparse matrices, corresponding to the construction matrix E, weight matrix U, coefficient matrix V, and auxiliary matrix Z, respectively, and setting the initial hyperparameter value μ; determining the target equation and the maximum number of iterations, and iteratively solving the construction matrix E, weight matrix U, coefficient matrix V, auxiliary matrix Z, Lagrangian operators Y1 and Y2 according to the update formula; when the number of iterations is the maximum or the iterations converge, outputting the final coefficient matrix V to obtain the clustering result. The present invention improves the objective function construction method, adds better constraints to capture deep-level information features, thereby improving the robustness and accuracy of image classification and obtaining better clustering effects.

The application discloses a kind of electroencephalogram emotion signal recognition method based on graph regular non-negative matrix decomposition, comprising the following steps: step one, using nearest neighbor method for electroencephalogram emotion signal constructs an adjacent matrix;Step two, establish a graph regular non-negative matrix decomposition model, and the non-negative constraint of matrix after decomposition is carried out;Step three, introduce a projection matrix, further to matrix implement three decomposition;Step four, the model is optimized;Step five, using the new representation PX obtained after the multiplication of trained model parameter P and sample X replaces the representation matrix V of original model, joins corresponding label matrix and trains a classifier in linear SVM classifier;Step six, the class of test sample is predicted by inputting into classifier.The application can effectively identify electroencephalogram emotion signal, compared with other classic electroencephalogram emotion recognition method, the application effectively improves recognition rate.

The invention relates to an electromagnetic wave rain attenuation completion measurement method based on non-negative matrix factorization, and belongs to the field of wireless communication. According to the method, firstly, a linear regression model is used for carrying out preliminary filling on missing values in a matrix, then data of other known columns are used as features to fit the linear regression model, the model is used for predicting the missing values so as to improve the initialization accuracy, and finally, the initialized matrix is subjected to non-negative matrix factorization so as to improve the initialization accuracy. And by traversing different potential dimensions, selecting a dimension value which can maximize a correlation coefficient of a non-missing value between the reconstruction matrix and the measurement matrix as a final result. The method does not depend on a specific rain attenuation model or parameter setting, has high universality, and can meet the complementation requirements of electromagnetic wave rain attenuation data in different areas and under different parameter conditions.

To provide a sound quality improvement device, a sound quality improvement method and a program, which can improve sound quality of sound separated and extracted from an acoustic signal in which a plurality of sounds are mixed.SOLUTION: A sound quality improvement device 1 includes a separation unit 10 and an estimation unit 20. The separation unit 10 performs independent component analysis on first acoustic signals SA1(t) and SB1(t), which are obtained by recording composite sound of sounds emitted from a plurality of sound sources (a first sound source O1 and a second sound source O2) at different positions (microphones 2A and 2B) and separates the signals into second acoustic signals SA2(t) and SB2(t) for each sound source. The estimation unit 20 performs non-negative value matrix factorization on the first acoustic signal SB1(t) and the second acoustic signal SA2(t) and estimates a third acoustic signal S3(t) corresponding to the sound source on at least one sound source in the plurality of sound sources (first sound source O1 and second sound source O2).SELECTED DRAWING: Figure 1

The present application provides a facial image clustering method, apparatus, device and storage medium. It relates to the field of image processing technology. The method includes: inputting the required clustering data, which is a high-order graph regular non-negative matrix; calculating and constructing a high-order similarity matrix; constructing a high-order graph constraint; defining and initializing the basis matrices and representation matrices of each layer of the high-order graph regular non-negative matrix decomposition; obtaining the update formulas of the basis matrices and representation matrices of each layer of the high-order graph regular non-negative matrix decomposition; updating the basis matrices and representation matrices of each layer of the high-order graph regular non-negative matrix decomposition; obtaining a low-dimensional representation matrix; and outputting the clustering results. The present application is aimed at facial image data with complex similarities, and can capture more levels of similarity information, thereby improving clustering performance.

The application discloses a neural anatomical heterogeneity analysis method integrating standard model and non-negative matrix factorization, and belongs to the field of biomedical signal processing. The application comprises: preprocessing structural magnetic resonance imaging data of patients and corresponding healthy control groups to extract brain morphological indexes; establishing a brain morphological standard model of the patient through a transfer pre-training model to estimate a deviation score matrix of the patient's cortical thickness and surface area, and extracting a non-positive part to obtain a brain morphological atrophy deviation matrix; performing non-negative matrix factorization on the atrophy deviation matrix to generate a preliminary disease factor matrix and a weight composition matrix; and rearranging and sampling the deviation matrix, calculating an average half-decomposition stability coefficient and a reconstruction error to determine the disease factor and the weight composition under the optimal component number. The method of the application integrates spatial pattern information at the group level while retaining individual heterogeneity information, and provides a new technical means for precise diagnosis of neuropsychiatric diseases.

The invention belongs to the technical field of image processing, and particularly relates to a dietary tracking image processing method based on non-negative matrix blind source separation, which comprises the following steps: acquiring a dietary image, and preprocessing the dietary image; inputting the preprocessed image into a Unet network for food image classification; analyzing the classified food images by adopting a non-negative matrix factorization algorithm to obtain different types of food images; converting different types of food images into an HSV color space, and performing thresholding processing on the food images converted into the HSV color space; obtaining an independent food image according to the image after thresholding processing; calculating volumes of the individual food images; determining the nutrient intake of the user according to the volume of the food image and the type and number of the food image, and realizing diet tracking of the user; according to the method, spatial constraint non-negative matrix blind source separation is introduced, the local structure and spatial correlation of the image are reserved, stacked or semi-covered food materials with similar colors / textures are effectively distinguished, and the accuracy of component separation in a mixed scene is remarkably improved.

The invention discloses a robust non-negative matrix factorization hyperspectral image demixing method. The method comprises the following steps: step 1, calculating a noise estimation value and a noise estimation matrix of a hyperspectral image; step 2, constructing an improved weighted residual non-negative matrix factorization model based on the noise estimation value and the noise estimation matrix; 3, introducing a second-order adjacency relation to obtain a high-order graph, and carrying out Laplacian regularization to obtain a regularization term; 4, introducing a high-order graph Laplacian regular term to obtain a robust non-negative matrix factorization model based on global structure constraint; and step 5, realizing hyperspectral image unmixing based on the robust non-negative matrix factorization model of the global structure constraint. According to the method, a more robust and more accurate unmixing effect can be obtained, the internal structure of the hyperspectral image can be better protected, and the method is more suitable for scenes with more complex ground features, richer details or higher unmixing precision requirements.

This invention belongs to the field of computer data clustering technology and discloses a multi-view clustering method, apparatus, electronic device, and storage medium. The method includes: acquiring multi-view data to be clustered, wherein the multi-view data is one of images, videos, audio, and text; determining the Laplacian matrix, depth decomposition matrix, and global consensus embedding matrix for each view based on the original data matrix of the multi-view data; performing hypergraph regularized deep non-negative matrix decomposition on each layer of the original data matrix through the depth decomposition matrix to obtain the decomposition result; performing high-level semantic alignment and consensus representation fusion on the decomposition result through the global consensus embedding matrix to obtain the final layer representation of the view; determining the clustering indicator matrix of the final layer representation according to the objective function; and processing the clustering indicator matrix using the k-means clustering algorithm to obtain the multi-view clustering result. Through the technical solution of this invention, the accuracy of multi-view clustering is achieved.