Constrained Conceptual Decomposition Clustering Method Based on Depth Matrix

Abstract

The invention provides a constrained concept decomposition clustering method based on a depth matrix. The method comprises the following steps: S10 obtaining m images to be clustered and constructs knearest neighbor graphs according to the images to be clustered; S20, obtaining a corresponding data matrix X for each nearest neighbor graph, wherein the data matrix X comprises n data points, and decomposing the data matrix X by a non-negative matrix decomposition method to obtain a characteristic matrix W; S30 marks p data points in the data matrix X; S40, based on the characteristic matrix W and the marked data points, the objective function sigma of the constraint conceptual decomposition is established; S50, according to the objective function sigma, iteratively weighting the preset number of times to obtain the eigenmatrix Wi and the weight matrix Hi of each layer of the depth decomposition of the data matrix X, 1 <= i <= k; S60 Using k -Means clustering algorithm analyzes and clusters the eigenmatrices of each nearest neighbor graph. This clustering method uses marked and unmarked data points, and imposes the marked information on the objective function as a constraint, whichgreatly improves the recognition ability and the clustering accuracy.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a constraint concept decomposition clustering method. Background technique [0002] With the development of data communication, there is an increasing demand for image data processing, especially in the fields of computer vision and pattern recognition. Matrix factorization-based techniques, such as non-negative matrix factorization and concept factorization, have received extensive attention in data clustering, both of which are inherently unsupervised methods. [0003] As the scale of data collection increases, the efficiency of algorithms also needs to be improved. Naturally generated image data is generated by structural systems, and its degrees of freedom are usually much smaller than the external dimensions. In low-dimensional linear subspaces, the efficiency of image clustering can be significantly improved. Clustering methods based on matrix decomposition It is...

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Problems solved by technology

[0006] In view of the above problems, the present invention provides a constrained concept decomposition clustering method based on a depth matrix, which effectively solves the problem that in the process of extending concept decomposition to a semi-supervised algorithm in the prior art, it is impossible to ensure that data points of the same class are mapped to the same Indicate technical issues in the space

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