39 results about "Subspace clustering" patented technology

The invention relates to a low-dimensional subspace clustering method based on projection matrix guidance, and the method comprises the steps: extracting a light response non-uniformity PRNU noise residual error from input image data through employing a denoising filter, and constructing a PRNU feature data set of an image; performing feature dimension reduction on the feature data set by adopting a projection matrix method, constructing a projection matrix maintaining a geometric structure, mapping the projection matrix to a low-dimensional potential subspace, and further constructing a model for the subspace by utilizing a sparse self-representation method; constraint is applied to sparse self-representation in the low-dimensional potential subspace, and joint optimization is carried out through an augmented Lagrange multiplier method ALM and an alternating direction minimization ADM strategy to be used for efficient clustering of data in the low-dimensional potential subspace. According to the method, the projection matrix maintaining the geometric structure is constructed, the high-dimensional PRNU features are mapped to the low-dimensional potential subspace, the local neighborhood relation and the global distribution structure are reserved in the dimension reduction process, the calculation cost is reduced, and the clustering robustness and performance are effectively improved.

The invention discloses a semantic-driven intelligent agent capability discovery method and device oriented to the intelligent agent Internet, and the method comprises the steps: firstly generating an intelligent agent structured portrait which covers the three-dimensional information of skills, roles and states; semantic coding is carried out on the portrait, and the portrait is converted into a high-dimensional semantic vector; then, the vector is partitioned, a codebook is generated in each subspace in a clustering mode, and the codebook comprises representative vectors and serial numbers of the representative vectors; and quantizing the sub-vectors into numbers based on a codebook, and splicing the numbers to form discrete identification codes of the agent index. And when the index is updated, incremental maintenance is executed according to the distance between the new agent sub-vector and the vector in the codebook. Meanwhile, a generative retrieval model is trained, task query is directly mapped into discrete identification codes, historical and new task samples are mixed for continuous learning in training, and stability constraints are introduced. According to the method, through semantic portraits, a quantitative index mechanism and memory enhancement continuous learning, an end-to-end retrieval and rapid updating capability discovery scheme is constructed.

The invention relates to a consistent semantic enhanced single-step parameter-free multi-view clustering method, which comprises the following steps of: 1) automatically learning anchor points, and avoiding the problem that the quality of the anchor points is reduced due to the randomness of an anchor point selection strategy; (2) spectral clustering is converted into decoupling decomposition of a representation matrix, and single-step data processing is completed without depending on a subsequent clustering method; and 3) the method does not contain any parameter, so that the problem that the clustering quality depends on the parameter is eliminated. According to the method provided by the invention, the accuracy which can be achieved on a Dermatology data set is greatly improved on the same data set compared with the accuracy which can be achieved on the same data set through traditional anchor point-based multi-view subspace clustering. According to the method provided by the invention, the problem that parameters such as multi-view clustering anchor point selection are difficult to adjust can be effectively solved, the problem of optimizing flow splitting is solved, and the method does not need to depend on a subsequent clustering method, so that the multi-view data clustering precision is improved.

The invention relates to a deep embedded subspace clustering method based on double depolarization contrast learning. The method comprises the following steps: selecting a public image data set as a sample set and constructing a clustering model; the method comprises the following steps: firstly, pre-training an auto-encoder, then enhancing data of a data set, inputting the data into the encoder to obtain embedded representation of the data set, and clustering the data set by using a k-means method to obtain an initialized subspace base; secondly, training a clustering model, to be more specific, enhancing data of a data set, inputting the data into two encoders to obtain embedded representations of the enhanced data, performing debiased positive sampling and debiased negative sampling on the embedded representations, and reconstructing the embedded representations of the two enhanced data through a decoder; the distribution probability of each embedded representation in each subspace is calculated by utilizing a subspace base, so that the clustering category of each sample is determined. According to the method, the autoencoder is trained by optimizing the positive and negative sample structures and the sample reconstruction distance, and meanwhile, the subspace base is iteratively optimized, so that a high-quality clustering model is obtained.

This invention belongs to the field of radar signal sorting technology, specifically relating to a radar signal sorting method, program, device, and storage medium based on improved sparse subspace clustering. This invention constructs a complex network of radar pulse sequences based on the self-representation properties between radar pulse data points in the same subspace. It utilizes a generalized orthogonal matching pursuit algorithm to reduce the number of connections between pulses from different radars in the complex network, thereby reducing computational complexity. Borrowing the idea of ​​graph segmentation, it uses Laplace spectral decomposition to mine the geometric correlations of radar pulse signals from the same source, achieving efficient sorting of non-ideal radar signals under complex electromagnetic environments. Simultaneously, this invention designs an algorithm based on an energy correction severance threshold, which can effectively solve for the accurate calculation of the number of cluster centers. This invention can effectively solve the problem of mis-sorting caused by aliasing or missing parameters of complex radar operating conditions.

The application discloses a kind of concrete aging state evaluation methods based on intelligent sensor, it is related to intelligent sensor technical field, including: acquisition multi-source sensing data, pre-processing generation window characteristic vector sequence;Carry out environmental mirror difference entropy calibration, form continuous sample set;Sparse subspace clustering is executed, and state cluster label and reconstruction error are obtained;Implement time series gradient entropy driven event mapping, event sequence is generated in joint gradient entropy;Improved hawks process model is constructed, and continuous surface and trigger coordinate parameter are output;Matching stage risk weight matrix is generated state evaluation result;Form aging file, generate early warning information.The application realizes the stable quantitative evaluation and early warning of concrete aging state by the collaborative processing of sparse subspace clustering and improved hawks process intensity modeling.

The invention provides a malicious software package detection method based on script deep semantics, which comprises the following steps: acquiring an original software package compressed package set, extracting an installation script file set and generating a code complexity feature data set; based on the installation script file set, extracting a sensitive behavior context set under the abstract syntax tree; based on the sensitive behavior context set, generating a fusion feature vector set under a CodeBERT pre-training model; obtaining a complete feature vector set based on the code complexity feature data set and the fusion feature vector set; performing subspace clustering based on the complete feature vector set to generate a data space with an abnormal identifier; calculating an abnormal score data set based on the data space with the abnormal identifier, and generating a data space with the abnormal identifier and an outlier identifier under a preset outlier threshold value; and performing malicious detection on the compressed package set of the original software package based on the data space with the abnormal identifier and the outlier identifier. According to the technical scheme provided by the invention, the accuracy of detecting the malicious software package is improved.

The invention provides a point cloud normal estimation method and device based on local subspace clustering, electronic equipment and a storage medium, and the method comprises the steps: introducing a feature point weight evaluation mechanism based on covariance features and geometric structure indexes into a local neighborhood, and screening out feature points with high confidence; carrying out multi-subclass division and plane fitting on the local point cloud by adopting low-rank subspace clustering with priori weight constraint in the neighborhood of the feature points, so as to re-estimate the normal direction of the feature points on the subclass level; meanwhile, noise point filtering and a normal diffusion strategy based on adjacent non-noise points are combined, so that the method can more accurately describe a local geometric structure in a complex scene with noise, multi-structure aliasing and non-uniform sampling, the precision and stability of point cloud normal estimation are remarkably improved, and the method is suitable for popularization and application. And sharp features and edge details of the model can be better maintained.

The invention belongs to the technical field of machine learning and data mining, and particularly relates to a large-scale high-dimensional image data set-oriented compressible subspace clustering method, which comprises the following steps of: firstly, designing a dictionary representation learning model based on a partitioning mechanism to select part of samples to construct a small-scale dictionary to replace the whole original data; a bipartite graph construction method is ingeniously introduced by utilizing the thought of joint clustering, the problem that the bipartite graph cannot be directly constructed due to the fact that a coefficient matrix is not a square matrix is solved, and the relevance between a dictionary sample and a new input data sample can be fully considered. Under the Laplacian matrix rank constraint of the combination graph, the method can directly learn to obtain an optimal structured bipartite graph, can directly obtain a final clustering result, and does not need any post-processing process. In addition, an efficient optimization algorithm based on alternate iteration is further designed in combination with an augmented Lagrangian multiplier method to solve the model.

The invention discloses a multi-geophysical field joint inversion method based on physical subspace decoupling, and belongs to the field of geophysical exploration. According to the invention, the problem of poor inversion effect caused by difficult characterization of multivariate and nonlinear physical relations of different types of rocks under complex geological conditions in the existing structure coupling joint inversion method is solved; according to the method, K subspace clustering is utilized to dynamically divide underground model units into different low-rank subspaces according to physical property parameter characteristics in each inversion iteration, and then a low-rank constraint based on a Schatten-p norm is applied to a physical property parameter matrix in each subspace; the clustering-coupling process and the inversion iteration are alternately carried out, so that the division of the physical property subspace and the model updating in each subspace are mutually promoted, the self-adaptive optimization is realized, and the clustering-coupling process and the inversion iteration are carried out at the same time; and finally, high-precision and high-resolution reconstruction of multi-type rock physical property distribution in the complex underground structure is realized.

The invention provides a multi-view subspace clustering cancer subtype identification method based on self-reinforcement learning, and the method comprises the steps: firstly, extracting the potential feature representation of each view from multi-omics data through a potential feature learning module; then, clustering similar samples by using a self-expression learning module, and introducing initial graph information as a supervision signal to construct a self-expression coefficient matrix; secondly, inputting the matrix into a view image fusion unit, and fusing multi-view information to generate a consensus image; in addition, in order to further suppress noise interference in multi-omics data, a self-strengthening back propagation unit is introduced, a confidence matrix is generated by optimizing a self-expression coefficient, fusion loss back propagation is guided, the quality of the self-expression coefficient is iteratively improved, and a consensus graph is optimized; and finally, based on the optimized consensus graph, realizing cancer subtype identification by applying a spectral clustering algorithm. According to the method, the self-reinforcement learning strategy is introduced, the interference of noise on sample relation capture is effectively relieved, and the clustering performance is remarkably improved.

The application relates to a social network text target topic detection method based on sparse subspace clustering, and belongs to the technical field of computer data processing of natural language processing and social network data mining.The method writes text identification into target platform text content and interaction event records, carries out word segmentation, denoising and vectorization processing, reduces short text noise and event mismatch interference on subsequent analysis, constructs a social relationship graph, calculates edge confidence weight to form a graph regular constraint parameter, reduces the weight of a low-confidence screen edge in the constraint, suppresses relationship noise caused by organized manipulation from the source, solves sparse representation coefficients, constructs a similarity matrix, performs spectral clustering, enhances the separability of samples with similar semantics but different propagation modes, calculates semantic cohesion and propagation deviation, determines a target topic cluster by using a double threshold, extracts a key text set and a propagation evidence set, forms a reviewable target topic detection result, and improves target topic detection rate and reduces false positives.

The invention discloses a multi-view subspace clustering method for enhancing tensor Schatten P-norm, and relates to the field of multi-view data subspace clustering, the method comprises the following steps: firstly, modeling noise data, and simultaneously considering Laplace noise (l1-norm represents loss) and Gaussian noise (l2, 1-norm represents loss) in the data; noise pollution in the data is effectively removed, and a cleaner tensor structure is obtained. Then, a weighted tensor Schatten P-norm is applied to the denoised tensor, which not only approaches the original structure feature of the tensor, but also fully considers the contribution of different singular values to the clustering performance. According to the method, a tensor denoising model and a weighted tensor Schatten P-norm are integrated into a unified framework, so that effective analysis of complex data is realized, different singular values are endowed with different weights, and remarkable structural features in the data are highlighted.

The invention relates to the field of ideological and political course classification teaching, in particular to a subspace clustering method and device for ideological and political course teaching and a storage medium. The method comprises the following steps: extracting numerical feature vectors of ideological and political course data, performing normalization processing to obtain an input matrix, performing clustering analysis on the input matrix through a subspace clustering method to obtain a low-rank coefficient representation matrix, constructing an affinity graph, and based on the affinity graph, obtaining a clustering result by applying a spectral clustering algorithm. Dividing the original data set of the ideological and political courses into different clusters; according to the method, a non-convex truncation norm is used to constrain low-rank representation and a feature matrix at the same time, a truncation singular value square operator with soft rank constraint is used to enable a low-rank representation matrix to meet the size of a target rank, and a globally optimal solution is obtained in a closed form. The complexity and diversity of ideological and political course data are effectively dealt with, the accuracy and practicability of ideological and political course classification are improved, and implementation of ideological and political course personalized teaching and ideological and political course optimization is supported.

The invention discloses a kernel norm-based robust flexible multi-view subspace clustering method and system. The method comprises the steps of mapping original image data to a high-dimensional regeneration kernel Hilbert space through a kernel technique; learning a consistency kernel through a self-weighted multi-kernel learning strategy; introducing low-rank constraint into self-expression learning of the Hilbert space to obtain a similar matrix; and calculating in the similar matrix according to a k-means algorithm to obtain an image clustering result. According to the method, original image data are mapped to a high-dimensional regeneration kernel Hilbert space by adopting a kernel technology, so that nonlinear data are effectively processed, weights are automatically distributed for different kernels, the importance of different views is effectively considered, the influence of noise is considered, low-rank constraints are added in a similarity matrix, and the image quality is improved. Therefore, an optimal similarity matrix with a clear block diagonal structure is constructed, and a better image clustering result can be obtained.

The invention discloses a mining area illegal expansion remote sensing change monitoring method based on sparse subspace clustering, and relates to the technical field of mining areas, and the method comprises the following steps: obtaining mining area image data, carrying out the preprocessing of the mining area image data, obtaining consistent image data, and extracting an image feature tensor; constructing a self-expression model with sparse constraint based on the image feature tensor, performing subspace division, and extracting a target change region set according to a subspace division result; performing spatial superposition comparison on the target change area set and pre-obtained mining area legal boundary data to generate a newly added change area set, and performing index extraction to obtain a change area quantitative index set; and judging an illegal expansion risk level of the newly added change area set, and generating a change monitoring report according to the illegal expansion risk level and the consistent image data. According to the invention, through illegal expansion of the risk marking layer, a supervision department is supported to carry out rapid positioning, grading management and graph and certificate output.

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 invention discloses a vector plot style detection method based on depth variational reasoning and sparse subspace clustering, and the method comprises the steps: S1, supplementing a viewport, flattening a matrix, generating an attribute tuple, and assigning an index; s2, keeping an edge connection relationship to construct a three-layer hierarchical graph consisting of node-line segment sub-graphs, semantic segments and segment connection edges, and serializing the three-layer hierarchical graph; s3, node features are generated through the sub-graphs, and three types of edge embedding including stroking, filling and dotted lines are carried out; s4, reading a teacher fingerprint, establishing an orthogonal basis by using a cosine minimum vector, and outputting a calibration fingerprint; s5, amplitude suppression is carried out on the calibration vector, a sparse self-representation coefficient is solved by adopting an iteration soft threshold, and a sparse graph is established; s6, extracting first 10% of samples of each cluster residual error to form positive and negative pairs with the cluster prototype, freezing a feature layer, finely adjusting the network, and outputting a final abnormal score and a difference vector; and S7, tracing the difference dimension according to the abnormal score. According to the method, the evaluation efficiency, accuracy and teaching interaction level of batch SVG operation in digital art teaching are remarkably improved.

The application discloses a mining area expansion remote sensing change monitoring method based on sparse subspace clustering, and relates to the technical field of mining areas. The method comprises the following steps: obtaining mining area image data, pre-processing the mining area image data to obtain consistent image data, and extracting an image feature tensor; constructing a self-expression model with sparse constraints based on the image feature tensor, performing subspace division, and extracting a target change area set according to the subspace division result; performing spatial superposition comparison between the target change area set and pre-obtained mining area legal boundary data to generate a newly added change area set, performing index extraction, and obtaining a change area quantitative index set; determining the expansion risk level of the newly added change area set, and generating a change monitoring report according to the expansion risk level and the consistent image data. The application supports rapid positioning, grading management and map output through an expansion risk marking layer.

The present disclosure provides a point cloud normal estimation method and device based on local subspace clustering, electronic equipment and storage medium. By introducing a feature point weight evaluation mechanism based on covariance characteristics and geometric structure indicators in the local neighborhood, high-confidence feature points are screened out, and a low-rank subspace clustering with prior weight constraint is used in the feature point neighborhood to divide and fit the local point cloud, so as to re-estimate the normal of the feature point at the subclass level. At the same time, combined with the noise point filtering and the normal diffusion strategy based on the adjacent non-noise points, the method can more accurately depict the local geometric structure in the complex scene with noise, multi-structure aliasing and non-uniform sampling, significantly improve the accuracy and stability of the point cloud normal estimation, and better maintain the sharp features and edge details of the model.

The application discloses a kind of automatic extraction method and system of residential space form typical mode, belong to city space form calculation and environmental wind field and pollution diffusion assessment technical field.The method includes: obtaining residential boundary, building contour and layer information, constructs raster data set;Closed operation is implemented to building grid and hole is filled, and minimum continuous enclosing contour is generated by selecting minimum connected radius;Open operation is extracted to open place space using multi-scale, and enclosed building is deduced;Based on multi-wind direction and three height slices, the windward front edge is automatically identified, the windward interface density, the line rate and the gap distance are calculated;After the form index is grouped and pre-clustered, the CLIQUE subspace clustering algorithm is used to output the typical mode and its minimum feature description.The application can output parameterized geometric template, which is used for wind field and pollution diffusion simulation selection, and has interpretability and batch processing efficiency.

This invention provides a method, apparatus, and device for identifying grain origins based on hyperspectral images, relating to the field of image recognition technology. The method includes: acquiring hyperspectral sample images of grains from different origins using a hyperspectral imaging device; constructing a pseudo-RGB image of each hyperspectral sample image by selecting three representative bands; performing grain instance-level segmentation on each pseudo-RGB image to obtain a corresponding segmentation mask; extracting the average spectral information of each grain using the segmentation mask and constructing a spectral information matrix; sequentially performing principal component analysis, local linear embedding, and sparse subspace clustering on the spectral information matrix to obtain a dimensionality reduction matrix; training a fully connected neural network model based on the dimensionality reduction matrix to obtain a grain origin traceability model, and then performing grain origin traceability based on the grain origin traceability model. This invention can significantly improve the accuracy and processing efficiency of grain origin identification.

The invention particularly relates to a wave band selection method based on graph decomposition and subspace clustering consistent learning. The method comprises the following steps: acquiring hyperspectral image data; constructing a wave band selection objective function based on graph decomposition and subspace clustering consistent learning; introducing an auxiliary variable into the waveband selection objective function to obtain an equivalent objective function; a Lagrange function is constructed based on the equivalent objective function, the Lagrange function is a function comprising a dictionary matrix, an auxiliary variable and a subspace coefficient matrix, and an augmented Lagrange method is utilized to solve the equivalent objective function to obtain an optimal subspace coefficient matrix; and constructing a waveband similarity graph based on the optimal subspace coefficient matrix, executing spectral clustering on the similarity graph to obtain waveband cluster tags, selecting a waveband closest to a cluster center from each cluster, and combining all wavebands to obtain an optimal waveband set. According to the method, the performance and generalization ability of a band selection algorithm are improved.

The invention relates to the technical field of data analysis, and further relates to a cue word-driven crowd component and behavior conjoint analysis system, which comprises a cue word analysis and Copula space construction unit for receiving cue words comprising a target behavior field name, an intervention field name, a variable value domain description and an output field name; the Copula space sparse subspace clustering unit is used for constructing a data matrix based on the cue word analysis and the unit interval pseudo observation value table output by the Copula space construction unit; an intra-cluster causal Uplift estimation and double correction unit which independently executes a process for each low-dimensional dependency cluster obtained by the Copula space sparse subspace clustering unit; and the actionable population generation and conjoint analysis report output unit is used for sorting individual causal promotion scores obtained by the intra-cluster causal Uplift estimation and double correction unit in each cluster from high to low. According to the method, collaborative fusion of distribution uniformity, structure sparsity and causal consistency is realized, and the accuracy of crowd division is remarkably improved.

The invention discloses a decomposition deconvolution beam forming method based on subspace clustering, and belongs to the field of direction of arrival estimation in passive detection. According to the invention, the problem of poor target discovery capability of the existing deconvolution beam forming algorithm is solved. According to the spectral clustering-based subspace decomposition method provided by the invention, the covariance matrix can be adaptively decomposed into the strong signal subspace and the weak signal subspace according to the size of the characteristic value without estimating the number of information sources, so that the problem of array gain reduction of a traditional deconvolution beam forming algorithm in a strong interference scene is solved; and the target discovery capability is improved. According to the DOA estimation method provided by the invention, null constraint deconvolution beam forming and a signal-to-noise ratio-based fusion algorithm are introduced, so that energy leaked to weak signal subspace components by interference can be effectively suppressed under non-ideal conditions, the influence of strong interference on a weak target can be effectively suppressed, and the robustness of the algorithm and the target discovery capability are improved. The method can be applied to direction-of-arrival estimation in passive detection.

The application discloses a motor bearing fault diagnosis method based on multi-source flow data driving, comprising the following steps: 1, collecting the vibration acceleration signal and the temperature signal of the bearing; 2, obtaining the evidence source parameter of the vibration acceleration signal in step 1 based on a dynamic weighted sparse subspace clustering algorithm; 3, calculating the evidence source parameter of the temperature signal; 4, calculating the basic probability assignment function of the evidence source parameters of the vibration acceleration signal and the temperature signal and performing weighted D-S evidence fusion; 5, based on the weighted D-S evidence fusion in step 4, obtaining the fault type of the bearing and judging whether the clustering diagnosis model needs to be updated according to the PH test. The motor bearing fault diagnosis method based on multi-source flow data driving solves the problem that the current bearing fault diagnosis model often uses a single signal source as input and adopts an offline training plus online diagnosis strategy, resulting in poor adaptability of the model to faults.

This invention proposes a cancer single-cell type identification method based on optimal transport subspace clustering. By employing a local graph-guided learning strategy, this invention effectively addresses a common problem in deep subspace clustering: the tendency to converge to suboptimal solutions during self-expression learning, leading to poor clustering results. This strategy significantly improves the accuracy and robustness of the clustering process. Furthermore, this invention introduces the Wasserstein regularized self-expression learning method, combined with the optimal transport algorithm, greatly enhancing the model's ability to learn subspace structures in cell clustering tasks. This enables the model to obtain more robust and reliable subspace representations, thereby improving clustering quality.

The invention discloses a large-scale data-oriented feature self-weighted anchor graph learning and structured clustering method, which comprises the following steps of: performing information acquisition and feature extraction on a large-scale data object, and performing feature normalization preprocessing to obtain a data feature matrix; establishing a structured anchor graph learning model based on feature self-weighting; optimizing constraint conditions of the structured anchor graph learning model through an alternating direction multiplier method to obtain an anchor graph and a class label thereof; and calculating a clustering result of the data feature matrix through a class label propagation method. According to the clustering method, efficient graph learning and clustering can be carried out on large-scale data influenced by noise and redundant information in a complex scene, and the robustness and accuracy of a traditional subspace clustering method are improved.

The application relates to the technical field of data classification, and discloses a data classification method and system based on an improved TSK fuzzy classification model, which comprises the following steps: obtaining a data set to be classified, and dividing the data set to be classified into a training set and a test set; based on the training set, using a supervised enhancement type soft subspace clustering algorithm and a deep learning algorithm to respectively optimize the fuzzy rule antecedent and the fuzzy rule consequent of a pre-constructed TSK fuzzy classification model; inputting the test set into the optimized TSK fuzzy classifier model for classification processing to obtain a data classification result. The application respectively optimizes the fuzzy rule antecedent and the fuzzy rule consequent parameters of the TSK fuzzy classification model, thereby improving the adaptability of the model to high-dimensional data and complex data sets, and further improving the precision and efficiency of the model in data classification.

The application provides a multi-view incomplete image subspace clustering method, which comprises the following steps: preparing data in the form of a three-dimensional tensor, unifying the sizes of multiple three-dimensional tensors, and slicing the three-dimensional tensors in the same way to generate data of multiple views, wherein the data of one view is composed of a complete part and an incomplete part; obtaining a self-expression matrix of each view; fusing the self-expression matrices of the views to obtain a total self-expression matrix; filling the incomplete part; and making the self-expression matrices of the views have a low-rank characteristic and approximate a block diagonal structure. The method can effectively restore the missing features of multi-view incomplete three-dimensional tensor image samples and the subspace structure of incomplete multi-view data, and finally realizes unsupervised and effective clustering of the multi-view incomplete image data set.