83 results about "Matrix factorisation" patented technology

A correlation-aware adaptive codebook compaction system for multi-modal data compression that preserves cross-modal relationships while providing enhanced reconstruction quality. The system analyzes temporal and spatial relationships between different data modalities to generate correlation maps that guide compression decisions. A virtual management layer performs stream characterization and adaptive routing, while a processing pipeline implements primary codebook compression with mismatch handling for novel data blocks. High-entropy data segments receive pre-compression processing before codebook compression. Sequential registration data is processed through matrix factorization and dedicated matrix codebooks. The system continuously monitors data distribution characteristics and automatically retrains codebooks when drift thresholds are exceeded. A neural upsampling subsystem uses correlation information to guide cross-modal enhancement processes through modality-specific networks and attention mechanisms. The unified output includes compressed data streams, correlation maps, synchronization metadata, neural model parameters, and updated codebooks, enabling synchronized reconstruction with preserved cross-modal relationships and enhanced quality through correlation-guided neural upsampling.

The invention relates to the field of environmental monitoring, and particularly discloses a non-negative matrix factorization and adaptive peak recognition fluorescence feature extraction and traceability system, which comprises a spectral data preprocessing module, a spectral data non-negative matrix factorization module, a component number automatic selection module, an adaptive peak recognition module, a feature library construction module and a similarity comparison module. An improved non-negative matrix factorization model is adopted to decompose the three-dimensional fluorescence spectrum matrix of a single sample, and an optimal component number K is automatically determined through multiplicative update rule iterative optimization; the self-adaptive peak identification module carries out selective filtering, accurately extracts the position and intensity of a fluorescence peak through multiple mechanisms, and carries out peak position calibration in a neighborhood; the Hungary algorithm is adopted to carry out characteristic peak matching to calculate the comprehensive similarity between the samples, and rapid and accurate identification of the pollution source is realized. The method has the advantages of high resolution, strong anti-interference capability, low requirement on the number of samples, automation and the like, and is suitable for water quality fingerprint feature extraction of a water sample in a complex environment and real-time source tracing of sewage.

The invention provides an educational resource sequence recommendation method and a related device, and relates to the technical field of resource recommendation. After student learning behavior data, learning resource attribute data and knowledge graph structure information are obtained, a regularization matrix decomposition technology is adopted to carry out potential factorization modeling on a student-resource interaction scoring matrix, and basic preference embedding features of students and resources are extracted. Aiming at diversity and complexity of student learning behaviors, a multi-view feature coding module is designed, learning sequence and short-term interest features are extracted from a behavior sequence view, static attribute features of students and courses are extracted from an attribute information view, and knowledge point pre-correction dependence and association relationship features between courses are extracted from a knowledge structure view. In order to improve the consistency and discrimination of feature representation, on the basis of multi-view fusion features, a comparative learning mechanism is introduced, and the discrimination and migration modeling ability of the model to student interests and preferences is enhanced by constructing a positive and negative sample comparison loss function.

A hybrid recommendation system for generating personalized film recommendations, the system comprising the following: a processing unit for receiving metadata, configured to receive descriptive data related to films, the descriptive data including plot summaries, genre identifiers, cast lists and director names; a text preprocessing module that is communicatively coupled with the metadata ingestion processing unit and is configured to perform tokenization, stopword removal, stemming, and lemmatization on the descriptive data to obtain a processed corpus; a TF-IDF vectorization unit configured to encode the processed corpus into high-dimensional semantic feature vectors by calculating TF-IDF inverse document frequency values ​​over the entire film dataset, with the semantic feature vectors representing the contextual meaning of terms for individual films; a collaborative filter engine comprising a user-element interaction matrix, wherein the engine is configured to compute latent preference signals using one or more techniques selected from the group consisting of cosine similarity, k-nearest neighbor similarity, and matrix factorization; a score fusion controller coupled to both the TF-IDF vectorization unit and the collaborative filter engine, wherein the module is configured to normalize the semantic feature vectors and the collaborative preference scores and dynamically combine them according to an adaptive weighting coefficient, the coefficient being determined as a function of data density, interaction sparsity, and user history length; and a recommendation output module configured to evaluate candidate films for each user based on the combined score and generate a top-N recommendation list.

The invention relates to a disturbance source positioning method based on dynamic mode decomposition, which belongs to the technical field of power system operation and analysis and comprises the following steps of: accurately extracting oscillation mode characteristics of a system by performing matrix decomposition on actually measured data; furthermore, a participation factor analysis model fusing time-space characteristics is constructed, and the spatial distribution characteristics and the time evolution characteristics of the modal are comprehensively considered, so that accurate positioning of the oscillation source is realized. Compared with a traditional method, the method has the advantages of no dependence on a detailed system model, high positioning precision and the like, is realized completely based on measurement data calculation, avoids complex system modeling, and has relatively high practical application value and popularization potential.

The invention relates to a matrix decomposition device and method based on a memristor cross array, which are suitable for efficient hardware implementation of singular value decomposition (SVD), the memristor cross array receives a conductance value mapped by a Grubrum matrix constructed by a to-be-decomposed matrix and a bit line input voltage converted by a bit line column vector, and outputs a corresponding current; converting the corresponding current into a voltage vector; performing normalization processing on the voltage vector to obtain a normalized vector; judging whether convergence occurs or not; performing normalization processing on the steady-state voltage vector to obtain a final output vector; and according to the final output vector, main characteristic values are extracted based on a first normalization circuit, and main singular values and corresponding singular matrixes are calculated. Compared with the prior art, the high parallel computing characteristic of the memristor cross array is utilized, efficient operation and hardware acceleration of matrix decomposition are achieved, the method has the advantages of being low in power consumption, high in speed and good in expansibility, and the method is suitable for application scenes such as artificial intelligence, signal processing and large-scale matrix operation.

The invention discloses an emotion recognition method based on multi-modal signal fusion, and relates to the technical field of emotion recognition, and the method comprises the steps: obtaining an electroencephalogram signal and a peripheral physiological signal to form a multi-modal signal, and carrying out the preprocessing of down-sampling, baseline correction and band-pass filtering on the multi-modal signal; based on the preprocessed signal, extracting a difference entropy feature and a power spectrum density feature; mapping the extracted features into a standardized space grid to generate a feature tensor with a uniform structure; performing adaptive weighting on the mapped feature tensor by using a frequency band fusion attention mechanism, and generating a frequency band weight through global average pooling and a full connection layer; inputting the weighted features into a full connection layer of the parameterized hypermatrix, and performing feature compression and modeling through matrix decomposition and reconstruction; a multi-task learning framework is adopted, classification results of emotion titer and awakening degree are output at the same time based on compressed features, and multi-task collaboration is optimized through a shared feature layer and a dynamic loss weight.

The invention discloses an unbalanced classification method for electroencephalogram data in epilepsy detection, and relates to the technical field of data optimization and big data processing. According to the method, firstly, covariance matrixes of majority class samples and minority class samples are calculated, linear transformation is achieved through matrix decomposition, and the minority class samples inherit global distribution characteristics of the majority class samples; and then, in the transformed feature space, sorting samples based on mahalanobis distance and performing partition pairing, selecting sample pairs with large difference to generate convex combination synthesis samples, and ensuring sample diversity and boundary consistency. Experimental results show that on a CHB-MIT electroencephalogram data set, the method effectively solves the problems that a traditional oversampling technology is prone to expanding minority class decision boundaries and generated samples are lack of diversity, and the reliability of epileptic seizure detection is remarkably improved.

The invention discloses a fitness quality evaluation method based on myoelectricity and images, which comprises the following steps: step 1, data acquisition: the acquired data comprises original myoelectricity data with time sequence information and time sequence image data of fitness actions; step 2, preprocessing the collected data; step 3, for the preprocessed myoelectricity data, extracting myoelectricity characteristics through a sliding window by adopting a time domain intermediate frequency value model, and then decomposing a muscle activation degree matrix into a collaborative structure factor matrix and an activation coefficient matrix through a non-negative matrix decomposition algorithm; for the preprocessed time sequence image data, adopting a BlazePose algorithm to extract 33 3D articulation points in the time sequence image data and calculating the Euler angles of the articulation points so as to obtain attitude angle features; and step 4, using a support vector machine to fuse the myoelectricity features and the attitude angle features to carry out action scoring. The method can effectively improve the fitness quality of the user.

The invention relates to the technical field of industrial process soft measurement and quality control, and discloses a paper quality prediction method based on multivariate statistical latent variable fusion and space-time transformation, which comprises the following steps: acquiring space-time sequence data of a multi-source sensor in a papermaking process, constructing a space-time diagram structure reflecting a topological relation of equipment, and preprocessing. Then, multi-view latent variables are extracted through non-negative matrix factorization, independent component analysis and robust principal component analysis, attention fusion is conducted on the latent variables through an LV fusion module, and fusion latent variables are obtained; and inputting the fusion latent variable and original node data into a multi-scale convolution auto-encoder to obtain spatial feature embedding, and inputting the spatial feature embedding and the fusion latent variable into a space-time Transform module together to realize joint modeling of space correlation and time dependence. And finally, outputting a paper quality predicted value through a linear regression module. The method can achieve the accurate prediction of the paper quality under a high-dimensional and multi-noise working condition, and is suitable for online monitoring and modeling updating.

This application provides a cross-modal semantic alignment method, system, and storage medium. The method extracts multimodal carbon features and performs standardized preprocessing, then uses a projection algorithm to map them to a low-dimensional common semantic space. It combines matrix factorization and association learning models to construct cross-modal semantic associations, and finally generates alignment results through large-scale model fusion inference. This solves the problems of insufficient accuracy and poor semantic coherence in cross-modal semantic alignment of traditional methods.

This invention discloses a multi-sensor fusion mapping method and apparatus for degraded scenarios, comprising: a front-end fusion odometry method: receiving multi-source data and obtaining initial pose through coordinate system alignment; performing point cloud distortion removal through IMU integration and extrinsic parameter transformation to generate a priori LiDAR pose; analyzing point pair contribution vectors based on ICP Hessian matrix decomposition to determine pose degradation; for non-degraded frames, constructing LiDAR point-to-surface residuals and RTK pose residuals, updating the state through ESIKF, and outputting keyframe pose, point cloud, and degradation labels; a back-end factor graph optimization method: filtering non-degraded keyframes through a sliding window, optimizing pose through point cloud beam adjustment; performing secondary verification of RTK pose to remove invalid values; constructing odometry, loop closure, and RTK factors, and fusing and optimizing to obtain a globally consistent high-precision point cloud map. This method enhances robustness through degradation detection and ensures global consistency through factor graph fusion, making it suitable for mapping complex scenarios.

A processing device (400) for performing non-negative matrix factorization on measured X-ray powder diffraction profiles comprises a measurement profile acquisition section (410) for acquiring a plurality of measured profiles, a decomposition section (420) for applying non-negative matrix factorization to the measured profiles and for calculating base profiles; an index calculation section (430) for acquiring the base profiles and for calculating indices based on a non-uniformity of the base profiles; a base profile classification section (440) for classifying the base profiles into a plurality of groups based on the indices; and a base profile correction section (450) for performing a correction based on the indices on at least one of the plurality of groups and for calculating a corrected base profile.

The application discloses an unsupervised hyperspectral image super-resolution method based on a matrix decomposition network and belongs to the technical field of hyperspectral image processing. The application is used for processing a hyperspectral image, generating a simulated low spatial resolution hyperspectral image Y and a high spatial resolution multispectral image Z; first, inputting the generated data pair (Y, Z) into a designed auto-encoder network, training iteration to obtain a point spread function and a spectral response function; for a target high spatial resolution hyperspectral image X, the target high spatial resolution hyperspectral image X can be assumed to be a linear combination of a terminal member matrix A and a corresponding abundance matrix S, that is, X = AS, a spectral and spatial degradation model is combined to model, a deep CP decomposition module is designed to calculate A, A and S are iteratively solved, and finally a fusion result is obtained. The application can obtain more rich spectral and spatial features, obtain a better fusion result, and has good performance in practice.

The invention discloses a remote sensing image sub-region rapid classification method based on dual-scale multi-graph regularization non-negative matrix factorization, and provides a dual-scale basis selection strategy and a multi-graph regularization non-negative matrix factorization model for overcoming the defects of complex scene adaptation, small sample learning and classification speed and accuracy balance in the prior art. Unsupervised subregion classification is realized through the steps of data preprocessing, double-scale seed set division, basis matrix construction, double information graph regularization item construction, model optimization solution and the like. According to the method, the classification number does not need to be predefined, the basis matrix representativeness is enhanced through dual-scale basis selection, and the classification precision and robustness are improved by combining a dual-graph regularization item and utilizing a manifold structure and discrimination information of data at the same time; the efficient optimization solution of the non-negative matrix factorization realizes the rapid classification of the sub-regions, and improves the accuracy and efficiency of the classification of the sub-regions of the remote sensing image.

This invention discloses a water pollution data monitoring method and system based on multi-objective analysis, belonging to the field of water pollution source tracing technology. The method includes: collecting a GIS map of the target watershed and time series data on rainfall, flow, and water quality; hydrologically segmenting the flow series according to the opening and closing status of dams to obtain surface runoff and baseflow components; constructing a two-dimensional water quality observation matrix, setting pulse and delay constraints in conjunction with rainfall and runoff components, and using a non-negative matrix factorization algorithm with ratio constraints for source analysis to obtain a source feature matrix and a source contribution time coefficient matrix; identifying pollution source emission attributes based on the ammonia nitrogen to total phosphorus ratio and the correlation between the source contribution coefficient and rainfall in the source feature matrix, and marking them on the GIS map. This invention integrates dam-controlled hydrological segmentation with matrix factorization based on physicochemical constraints to achieve refined source tracing, improving the accuracy and interpretability of pollution source identification.

The invention relates to the technical field of power customer service and automatic matching, in particular to an automatic matching method for power customer engineering. Comprising the following steps: screening out candidate service units meeting qualification conditions by using a multi-attribute decision analysis method; based on a content-based recommendation algorithm, extracting features of customer engineering, and recommending electric power engineering service units matched with the features; according to a recommendation algorithm based on matrix decomposition, power customer engineering and power engineering service units are expressed as rows and columns of a two-dimensional matrix respectively, and service units with potential matching are recommended by calculating the incidence relation between matrixes; the recommendation results of the two algorithms are fused, other factors are considered, sorting is carried out, and the final recommendation result is comprehensively generated. According to the design, automatic matching between the customer and the electric power engineering service unit can be realized, the matching success rate is improved, the transaction cost is reduced, and the operation efficiency of the electric power market is improved.

The application discloses a hyperspectral anomaly detection method and device based on factor group sparse regularization and belongs to the technical field of remote sensing image processing. The method reduces three-dimensional hyperspectral data into a two-dimensional matrix form, decomposes the matrix into a background part and an anomaly part based on a background dictionary; utilizes a Schatten-p norm to regularize and constrain the low-rank characteristics of the background part, utilizes a 2,1 norm to regularize and constrain the column sparse characteristics of the anomaly part; converts the Schatten-p norm into a factor group sparse regularization form, introduces an auxiliary matrix to replace variables after performing singular value decomposition on a coefficient matrix; iteratively solves the replaced variables by using an alternating direction multiplier method to obtain an anomaly matrix and calculates an anomaly detection value of each pixel. The application avoids a complex calculation process, guarantees detection accuracy and significantly improves detection speed.

The invention discloses a biological entity multivariate association prediction system combining linear and nonlinear fusion matrix factorization, which relates to the technical field of biological entity multivariate association prediction and comprises a multivariate biological entity input module, a cross-modal feature extraction module, a dual-channel fusion matrix factorization module, a dynamic feature fusion device and a multivariate association prediction engine. According to the method, the limitation of a traditional biological entity association prediction method is broken through by fusing linear and nonlinear matrix decomposition technologies, and a dynamic feature fusion mechanism realizes optimal combination of cross-modal features through adaptive weight adjustment, so that the problem of insufficient flexibility of a traditional static fusion strategy is overcome; in addition, the system adopts a three-dimensional tensor modeling technology, a unified prediction framework of multiple types of associations such as gene-disease, drug-target, metabolite-pathway and the like is realized, heterogeneity characteristics of biological associations can be effectively maintained, and compared with the prior art, the analysis capability of a complex biological network is remarkably improved.

The present invention discloses a drug sensitivity classification method, system and equipment based on multi-omics data integration, which relates to the field of drug sensitivity classification. The method of the present invention uses joint non-negative matrix decomposition to decompose multiple omics matrices of all multi-omics data samples, and the obtained shared matrix represents the linear integration features of all samples; at the same time, a variational autoencoder (VAE) is used to obtain the nonlinear integration features of all samples; the spliced ​​feature vector obtained by splicing the linear integration features, the nonlinear integration features and the drug features is used for drug sensitivity classification. The linear integration features contain important information of all omics and eliminate the noise in the original data. The nonlinear integration features contain nonlinear and complex relationship information in the multi-omics data. The present invention utilizes the complementarity of the two features to mine the potential information between the multi-omics data, thereby greatly improving the accuracy of drug sensitivity classification.

The invention discloses an outer limb robot self-adaptive impedance control method and system based on a motor function. According to the method, motion information and EMG / EEG multi-mode signals are collected, a motion intention is predicted through weighted stacking and nonlinear compensation, the weight is updated based on an admittance model, and a planning trajectory is learned iteratively; the motor function index is obtained by decomposing the electromyographic signal through a non-negative matrix, the damping and rigidity are dynamically adjusted through feed-forward-impedance double-loop control, and the system stability is verified through the passivity theory. According to the method, the self-adaptive response of the outer limb robot to the human body motor function change is realized, the accuracy and safety of man-machine interaction are improved, and the method is suitable for outer limb robot application scenes such as rehabilitation training and industrial assistance.

The invention relates to the technical field of signal processing, in particular to a filtering method for dealing with a system with complex multiplicative noise and random time delay uncertainty, which designs a reasonable system model of a multi-sensor networked system meeting conditions, and adopts a maximum and minimum robust estimation principle, a de-randomization method and a virtual noise technology. The system is converted into a multi-model multi-sensor system only with uncertain noise variance, an augmented noise method, a non-negative definite matrix factorization method and a Lyapunov equation method are applied, two robust centralized fusion steady-state Kalman estimators are obtained, and the robustness of the estimators is further proved. The method can be used for solving the problem of robust fusion Kalman filtering of multi-sensor single-channel ARMA signals with random parameter matrixes, uncertain noise variance and networked random uncertainty.

A method performed by an electronic device, includes: receiving a calibration dataset corresponding to a task relevant to a user; providing the calibration dataset as an input to a trained teacher model and an on-device model that are stored in the electronic device; updating the on-device model by, for each layer of the on-device model,: determining covariances between the trained teacher model and the on-device model based on: differences between feature maps generated by the trained teacher model and the on-device model, and differences between weights in the trained teacher model and weights in the on-device model; and modifying the weights for the on-device model using matrix decomposition on weight matrices in the trained teacher model based on the determined covariances.

The invention discloses a user development analysis method for video recommendation. The method comprises the steps of data acquisition, user development cycle stage transfer, stage transfer graph generation, video abnormal recommendation detection and report generation. The invention belongs to the technical field of user analysis, and particularly relates to a user development analysis method for video recommendation. According to the scheme, a basic matrix decomposition model is adopted to learn basic interests of users and core features of videos; perceiving the current state of the user through a state vector, combining a short-term preference score and a Q function for measuring a long-term value to make recommendation from candidate videos, and calculating a reward function to quantify the long-term value of a recommendation action for user development cycle transfer; a sliding window is adopted to generate a time sequence, an encoder is utilized to extract potential feature distribution in the window to capture a system health mode, a weighted accumulation reconstruction error is calculated to serve as an anomaly scoring function, a dynamic threshold value is set, and accurate perception of the abnormal state of the recommendation system is achieved.

The application discloses a hyperspectral anomaly detection method and device based on factor group sparse regularization and belongs to the technical field of remote sensing image processing. The method reduces three-dimensional hyperspectral data into a two-dimensional matrix form, decomposes the matrix into a background part and an anomaly part based on a background dictionary; utilizes a Schatten-p norm to regularize and constrain the low-rank characteristics of the background part, utilizes a 2,1 norm to regularize and constrain the column sparse characteristics of the anomaly part; converts the Schatten-p norm into a factor group sparse regularization form, introduces an auxiliary matrix to replace variables after performing singular value decomposition on a coefficient matrix; iteratively solves the replaced variables by using an alternating direction multiplier method to obtain an anomaly matrix and calculates an anomaly detection value of each pixel. The application avoids a complex calculation process, guarantees detection accuracy and significantly improves detection speed.

The invention relates to the technical field of gas spectrum detection, and discloses a noise suppression non-negative matrix factorization gas cross interference removal measurement method and system, the noise suppression non-negative matrix factorization gas cross interference removal measurement system comprises a gas supply module, a detection module and a control analysis module, the gas supply module comprises a plurality of gas tanks, a dynamic gas mixing device, a pressure controller and a one-way valve, the plurality of gas tanks are respectively and independently connected with a gas inlet of the dynamic gas mixing device to form a plurality of independent gas paths, and the pressure controller is arranged between a gas outlet of the dynamic gas mixing device and a gas inlet pipeline of the multi-way gas pool; and one-way valves for preventing backflow and cross contamination are respectively arranged on the gas inlet and the gas outlet of the multi-way gas tank. The method can stably separate and quantify the ethylene / ethane near-infrared weak spectrum band with weak absorption and serious absorption spectrum overlapping, has obvious anti-noise and anti-interference capabilities, and is convenient for miniaturization, low power consumption and batch deployment.

To provide an information processing system configured to obtain a highly accurate analysis result even for significant overlap of spectra originating from components.SOLUTION: An acquisition step A6 acquires target measurement data indicating a result of measurement using an X ray for a sample to be analyzed, and at least one piece of reference data. A designation receiving step A7 receives a designation of the reference data. A setting step A9 sets an objective function for executing non-negative matrix factorization, including a linear combination of at least one variable basis and at least one fixed basis, based on the designation. Each fixed basis corresponds to the designated reference data. The components of the variable basis are set to be variable parameters in the non-negative matrix factorization. The components of the fixed basis are set to be fixed parameters in the non-negative matrix factorization.SELECTED DRAWING: Figure 4

This invention relates to the field of image encryption and discloses an optical image asymmetric encryption method based on nonnegative matrix factorization. The encryption method includes the following steps: S1, generating a preliminary encryption result through a first optical transformation based on the original image and a first random phase mask; S2, generating an encrypted result through a second optical transformation based on the preliminary encryption result and a second random phase mask; S3, performing nonnegative matrix factorization on the encrypted result to generate an image matrix and a coefficient matrix; S4, using the coefficient matrix as ciphertext and the image matrix as the private key to complete image encryption. This invention significantly improves the security of optical encryption. It combines nonnegative matrix factorization with double random phase coding to construct an asymmetric encryption mechanism that effectively resists known plaintext, chosen plaintext, and special attacks, while also being robust to cropping and noise interference. The framework is highly scalable and applicable to image and audio data encryption.

The invention belongs to the technical field of machine learning, and particularly discloses a missing data prediction method based on sparse Bayesian learning and a coevolution algorithm, and the method comprises the steps: carrying out the matrix decomposition of an original data matrix corresponding to flight historical data based on a set matrix decomposition parameter, and obtaining a user factor matrix and an article factor matrix, extracting global features; performing feature division based on the article factor matrix into a plurality of feature subspaces; respectively optimizing the sparse Bayesian model parameter of each feature subspace by using a target optimization algorithm until a set co-evolution parameter is reached, obtaining a trained sparse Bayesian model to carry out local prediction, and obtaining a model prediction value of a missing position as a local feature; and carrying out feature fusion on the global features and the local features to obtain complete missing value prediction data. According to the method and the device, the accuracy of missing value prediction can be improved, and the balance between precision and uncertainty evaluation is realized while the calculation efficiency is ensured.

The invention relates to the technical field of image processing and remote sensing analysis, and provides a robust depth non-negative matrix factorization method for hyperspectral image unmixing, which comprises the following steps of: decomposing hyperspectral data into a product of multiple layers of non-negative low-rank matrixes by adopting a multi-layer non-negative matrix factorization method; expressing similarity and difference of data in each layer of non-negative low-rank matrix based on a double-graph adversarial learning mechanism; structural sparse regularization based on an inner product is adopted to constrain a Gramb matrix in the decomposition process; and constructing a comprehensive optimization model to obtain an unmixing result of the hyperspectral data. According to the method, hyperspectral data are expressed as a plurality of non-negative low-rank matrixes, and meanwhile, an adversarial graph regular term, a hierarchical sparse constraint and a truncation activation mechanism are introduced to improve the robustness and expression ability of the model. And finally, performing efficient optimization by using an alternating direction multiplier method (ADMM), so that the provided method can accurately extract end members and abundance in a complex noise environment, and stable hyperspectral image unmixing is realized.