155 results about "Nonnegative matrix factorisation" patented technology

The invention discloses a face identification method based on non-negative matrix factorization and a plurality of distance functions. The method comprises the following steps of: extracting face image characteristics, performing the non-negative matrix factorization on all face images in a learning image library to obtain a corresponding basic image and a weight vector, namely a characteristic vector corresponding to each learning image; performing characteristic extraction on all of test images by utilizing a non-negative matrix factorization algorithm to obtain a characteristic vector corresponding to each test image; calculating a mean characteristic vector Hm corresponding to each type of training sample image set with known identity by utilizing the obtained characteristic vectors; and calculating similarity between each test image and the mean vector corresponding to each type of training sample image set, obtaining a quantized numerical value of the similarity by combining the different distance functions, finding a training image type which is proximate to the test image, namely the nearest neighbor point, and classifying the test image into a type with the nearest neighbor point according to a nearest neighbor classification method to finish the identification of all the test images.

The invention discloses a self-adaptive hyperspectral image unmixing method based on region segmentation. In consideration of coexistence of linear mixing and bilinear mixing, the method is implemented by adopting the following steps: inputting a hyperspectral image; estimating the number of end elements with a minimum error based hyperspectral signal recognition method; extracting end element matrixes with a vertex component analysis algorithm; clustering hyperspectral data with a K-means clustering method, and segmenting the image into a homogeneous region and a detail region; adopting a linear model for the homogeneous region and performing unmixing with a sparse-constrained non-negative matrix factorization method, and adopting a generalized bilinear model for the detail region and performing unmixing with a sparse-constrained semi-non-negative matrix factorization method. According to the method, characteristics of the hyperspectral data and abundance are combined, the hyperspectral image is represented more accurately, and the unmixing accuracy rate is increased. The sparse constraint condition is added to the abundance, the defect of high probability of local minimum limitation of the semi-non-negative matrix factorization method is overcome, more accurate abundance is obtained, and the method is applied to ground-object recognition for the hyperspectral image.

The invention discloses an image retrieval method based on sparse non-negative matrix decomposition. It includes the following steps: 1) Query and extract the image and accompanying text of the retrieval results under two different image data sources; 2) Extract the tags in the accompanying text, and filter the results according to word frequency to form a vocabulary; 3) For each Image set, using the association relationship between labels and images to form an association matrix between labels and images; 4) Using sparse non-negative matrix decomposition to analyze the association matrix obtained in step 3), to obtain the shared subspaces of different sources of data and their corresponding independent subspaces Subspace; 5) The user sends a retrieval request for images on a data source, forms a query vector and maps it to the corresponding subspace of the data source, calculates the similarity with all images and sorts them, and returns the top N most similar images. The present invention makes full use of the associated knowledge of tags and images under multiple data sources, performs migration learning through sparse non-negative matrix decomposition, and improves the accuracy of image retrieval on target data sources.

The invention belongs to the technical field of image processing, and particularly discloses an SAR image target recognition method based on non-negative matrix factorization of sparse constraint. According to the method, more effective features are extracted by improving a non-negative matrix factorization method to improved recognition precision, and the problems that features extracted in the prior art are not typical and recognition precision is not high are solved. According to the method, a training sample image and a test sample image are pre-processed in the same way, logarithmic transformation is carried out, a pre-processed training sample set is factorized through non-negative matrix factorization of sparse constraint to obtain a basis matrix and a coefficient matrix, a test sample set is projected in a sub space of a basis matrix structure, classification is carried out through an SVM after a feature matrix is obtained, and then classification accuracy is obtained ultimately. Compared with the prior art, the extracted features are more effective and recognition precision can be effectively improved.

Supervised nonnegative matrix factorization (SNMF) generates a descriptive part-based representation of data, based on the concept of nonnegative matrix factorization (NMF) aided by the discriminative concept of graph embedding. An iterative procedure that optimizes suggested formulation based on Pareto optimization is presented. The present formulation removes any dependence on combined optimization schemes. Analytical and empirical evidence is presented to show that SNMF has advantages over popular subspace learning techniques as well as current state-of-the-art techniques.

Non-negative matrix factorization, NMF, is combined with identification of a maximum margin classifier by minimizing a cost function that contains a generative component and the discriminative component. The relative weighting between the generative component and the discriminative component are adjusting during subsequent iterations such that initially, when confidence is low, the generative model is favored. But as the iterations proceed, confidence increases and the weight of the discriminative component is steadily increased until it is of equal weight as the generative model. Preferably, the cost function to be minimized is:

The invention discloses a non-negative matrix factorization-based heart and lung sound separation method. The method comprises the steps of firstly filtering a mixed signal of heart and lung sounds in a time domain to obtain a priori heart sound signal and a priori lung sound signal; secondly performing short-time Fourier transform and sparse non-negative matrix factorization on the priori heart sound signal and the priori lung sound signal; and finally obtaining a basis matrix of the mixed signal. The sparse non-negative matrix factorization process is equivalent to a learning process of the basis matrix, and has a clustering effect; the basis matrix can be mined effectively from the priori heart sound signal and the priori lung sound signal; and a heart sound signal and a lung sound signal are accurately separated from the mixed signal of the heart and lung sounds in the time domain according to the basis matrix in the subsequent steps. The invention furthermore discloses a non-negative matrix factorization-based heart and lung sound separation apparatus, which has the abovementioned beneficial effects.

The invention relates to a non-negative matrix factorization and convolutional neural network-based color image processing method. The method comprises a step of obtaining a color image by acquisitionequipment and preprocessing the color image, wherein the preprocessing comprises the following steps of: extracting image features by adoption of a non-smooth non-negative matrix factorization algorithm; and combining a convolutional neural network to optimize a color image classification and reconstruction process: firstly reading an image data matrix to obtain a row number and a column number,initializing corresponding parameters, and combining the convolutional neural network to complete image classification and reconstruction. In color image processing, the image processing method is more accurate in feature extraction, few in reconstruction errors and high in classification efficiency.

The problem that in the prior art, the face recognition accuracy is low is solved. The invention provides an adaptive graph regularization non-negative matrix factorization method for face recognition. The method comprises the following steps: vectorizing a face picture; decomposing the data matrix X into a product of a base matrix and a coefficient matrix; adding a non-negative constraint to eachelement in the basis matrix and the coefficient matrix; constraining a coefficient matrix of the data matrix X; obtaining a data matrix X, obtaining a processed similarity matrix, processing the similarity matrix S by utilizing a diagonal matrix and a Laplace matrix, introducing a weight constraint item Lambda to obtain an adaptive graph, and finally introducing the adaptive graph as a regularization constraint item into a non-negative matrix factorization objective function of the data matrix X to identify the face photo. According to the method, the weight constraint term is introduced, theweight matrix is not kept unchanged any more, a large amount of noise and outliers are removed, local neighborhood structure information is complete, and the recognition accuracy is improved.

The invention discloses a method for recognizing upper limb and hand rehabilitation training action of a stroke patient. The method comprises the following steps: performing blind source separation onelectromyographic signal data by using a non-negative matrix factorization model, and removing non-stationary muscle activation information to obtain a stable time-varying blind source separation result; performing further pattern recognition by using the factorized time-varying blind source separation result data to improve the stability and accuracy of recognition; and making learning featuresmaintain time and space characteristics at the same time through a CNN-RNN model. The CNN-RNN model does not require manual data feature extraction and screening, directly processes the data, automatically extracts the features and completes classification recognition, can realize end-to-end rehabilitation training action recognition analysis, and is combined with an attention layer for attentionweighting of a hidden state of a second layer in a two-layer two-way GRU layer to give a greater weight to data with a greater contribution degree, so that the data can play a greater role, and the accuracy of classification recognition is further improved.

The invention discloses a post-stroke upper limb intermuscular collaborative coupling analysis method. The method comprises the following steps: firstly, synchronously acquiring and preprocessing multi-channel surface electromyogram signals in an upper limb movement process; then carrying out non-negative matrix factorization on the preprocessed data; presetting the number of decomposition layers,multiplying two decomposed matrixes to obtain a reconstructed data matrix, and calculating an explanatory variance; judging a cooperative and non-cooperative relationship; and finally, calculating the intermuscular coupling strength of the coherence measurement in different frequency bands. From the perspective of motion generation and execution, the coupling strength difference of collaborativemuscles in the muscle collaborative model is discussed, and a physiological basis can be provided for the motion rehabilitation process of stroke patients.

The invention discloses a multi-view data missing complementing method for multi-manifold regularization non-negative matrix factorization, which comprises the following steps of: obtaining manifold and global clustering in which unmarked multi-view data tends to be consistent by utilizing a multi-manifold regularization non-negative matrix factorization algorithm through consistency assumption among multiple views; and constructing a multi-view collaborative discrimination model by adopting a view collaborative improved Gaussian mixture method. Pre-calibration of a cluster to which a sample belongs is realized by calculating the cluster relevancy level of the sample with missing data under a non-missing view angle; and establishing a missing data prediction model under a specific view angle by utilizing the consistency of multiple view angles in a low-dimensional space and adopting a multiple linear regression analysis method, thereby realizing accurate data completion under a multi-attribute missing condition. According to the method, large-scale labeled samples are not needed for training, the pre-defined category relation and related characteristics are avoided, and the understanding and discovering capacity of an existing multi-view mining technology for unlabeled multi-source data is improved.

The invention discloses a data-to-text generation method based on fine-grained topic modeling. The data-to-text generation method comprises the following steps: learning semantic representation of each data record on a coding layer based on a bidirectional long-short-term memory network; learning topic distribution corresponding to each data record and word distribution corresponding to each topicbased on a non-negative matrix factorization method to obtain a topic word table corresponding to each data record; based on the semantic representation of each data record in a decoding layer, carrying out text generation by utilizing a long-term and short-term memory network, an attention mechanism and fine-grained topic representation in combination with a topic word table; and performing model training to obtain an optimal text generation result. According to the method, topic distribution of data and word distribution corresponding to topics are mined by utilizing a non-negative matrix factorization method, so that topic consistency between a generated text and a data table is restrained, and a model is guided to learn a more accurate word use mode; a copying mechanism is introducedin the text generation process, and it is guaranteed that the model can accurately generate numerical description.

A correlation analysis method for identifying Alzheimer's disease related biomarkers comprises the following steps: based on sparse non-negative matrix factorization of joint connectivity, projecting structural magnetic resonance imaging (SMRI), single nucleotide polymorphism (SNP) and gene expression data into a public feature space at the same time; and also adding the connectivity information and genetic data of each region of the brain as priori knowledge to identify a region of interest ROI, risk SNP sites and risk genes associated with Alzheimer's disease patients and early cognitive impairment patients. The method has higher correlation analysis capability and better anti-noise performance and biological interpretation performance.

The invention relates to a tumor genome copy number variation characteristic pattern recognition method and application thereof, and belongs to the technical field of gene diagnosis. According to themethod, systematic classification is carried out on tumor genome copy number variation characteristics, 80 numerical values of eight types of tumor genome copy number variation characteristics are specifically extracted, a copy number variation characteristic mode of a tumor sample is extracted by utilizing a non-negative matrix factorization algorithm, and then the tumor is typed according to thecopy number variation characteristic mode. Therefore, accurate prediction of clinical prognosis of tumors can be realized.

The invention discloses a micro-expression recognition method and device. The method comprises the following steps: solving a climax frame of a preset micro-expression video frame by combining an optical flow method and a dichotomy; obtaining N specific regions in each frame of the high tide feature graph set, wherein the high tide feature graph set is obtained by adding high tide frames and preset frames; obtaining an M-dimensional feature vector of each specific region through an optical flow parameter of each pixel obtained according to an optical flow method; inputting all the obtained feature vectors into a preset GRU model for feature extraction to obtain high tide dynamic features; calculating high tide static characteristics of the high tide frame according to a preset basis matrixand a local non-negative matrix factorization method; and fusing the high tide dynamic features and the high tide static features, and inputting the fused features into a preset classifier for classification to obtain a classification result. According to the method and the device, the technical problem of unreliable identification result caused by the fact that the existing micro-expression identification technology cannot completely obtain the characteristics of each layer of the micro-expression and has redundant information irrelevant to the expression is solved.

The invention relates to a user electricity stealing behavior identification method based on non-negative matrix factorization and density clustering. The method comprises the following steps: (1) preparing user electricity utilization data, including data source selection and data screening and cleaning; (2) selecting an electricity stealing behavior characteristic variable to obtain an originalelectricity stealing characteristic set; (3) extracting electricity stealing behavior characteristics based on non-negative matrix factorization; (4) establishing an improved DBSCAN electricity larceny behavior recognition model and training the model; and (5) performing electricity larceny suspicion screening on all users by utilizing the electricity larceny behavior model to obtain users with high electricity larceny suspicion degree, and checking and confirming on site by an electricity larceny inspector. Compared with a traditional electricity larceny checking mode that electricity larcenybehaviors are checked manually, the electricity larceny checking method improves the working efficiency and accuracy of electricity larceny checking, and is beneficial to reducing national electricity charge loss and reducing national property loss.

The invention discloses a network traffic data filling method and device, equipment and a storage medium. The method comprises the steps: carrying out the modeling of network traffic data into a three-dimensional original tensor, deeply mining the periodic features between the network traffic data, and reflecting the multi-dimensional features of the network traffic data; except regression and CPdecomposition are combined to construct a loss function, accurate recovery of data can be carried out in a targeted mode by selecting a set weight w, and accurate recovery of elephant flow data is achieved. Meanwhile, Execut regression can describe the central characteristics of the data and can describe the tail characteristics of the data, the full-view characteristics of the data are reflected,and the problem that local characteristics of all parts of the data cannot be described through a traditional method is solved; according to the method, the factor matrix is updated according to thenon-negative matrix factorization algorithm and Except regression, in the updating process, it is not needed to calculate an inverse matrix of the matrix like an ALS algorithm, it is also not needed to repeatedly balance an appropriate learning step length like an SGD algorithm, and the calculation complexity is greatly reduced.