Adaptive weighted constrained subspace block-diagonal representation image clustering method and system

The subspace block diagonal representation method with adaptive weighted constraints solves the problem of describing intra- and inter-class similarity and difference in high-dimensional data clustering, achieving higher clustering accuracy and stability, and enhancing robustness to outliers.

CN116681916BActive Publication Date: 2026-06-19GUILIN UNIV OF ELECTRONIC TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
GUILIN UNIV OF ELECTRONIC TECH
Filing Date
2023-06-07
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing technologies struggle to accurately describe the similarity and differences between intra- and inter-class data in high-dimensional data clustering, and are sensitive to noise and outliers, resulting in unstable clustering performance.

Method used

An adaptive weighted constraint subspace block diagonal representation method is adopted. By calculating the class weighted constraints and Pearson correlation weighted constraints of the data, the objective function of the diagonal representation model is established, and the self-representation coefficient matrix is ​​optimized and solved to construct an undirected weighted graph for spectral clustering.

Benefits of technology

It improves the accuracy and stability of clustering, enhances robustness to outliers, perfects the block diagonal structure of the self-representation coefficient matrix, and improves clustering performance.

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Abstract

This application discloses an adaptive weighted constraint subspace block diagonal representation image clustering method and system. The method includes: acquiring image data to be clustered and establishing a data matrix; calculating class weighting constraints based on the data matrix and Pearson correlation weighting constraints based on the image data; establishing a diagonal representation model objective function based on the class weighting constraints and Pearson correlation weighting constraints, and optimizing the self-representation coefficient matrix of the diagonal representation model objective function; constructing an adjacency matrix based on the self-representation coefficient matrix, and establishing an undirected weighted graph based on the adjacency matrix and image data; and performing spectral clustering processing on the undirected weighted graph to obtain cluster labels. This application integrates the block diagonal regularization of the self-representation coefficient matrix obtained from the linear reconstruction process with the class weighting constraints and Pearson correlation weighting constraints describing data relationships, thereby further improving the block diagonal structure of the self-representation coefficient matrix, enhancing robustness to outliers, and improving clustering performance.
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