A non-negative matrix factorization clustering method for a robust structure based on graph regularization

Abstract

The invention provides a robust structure non-negative matrix factorization clustering method based on graph regularization, and the method comprises the steps: S10, obtaining m to-be-clustered images, and constructing k nearest neighbor graphs according to the to-be-clustered images; S20, a corresponding data matrix Y is obtained for each nearest neighbor graph, the data matrix Y comprises n datapoints, and a non-negative matrix decomposition method is used for decomposing the data matrix Y to obtain a feature matrix W and a coefficient matrix H; S30, establishing an objective function O ofrobust structure non-negative matrix decomposition based on graph regularization based on l2 and p norms; S40, according to the objective function O, using an iterative weighting method to iterate preset times, and updating the feature matrix W, the coefficient item and the graph regular item; S50 analyzing and clustering the feature matrix W obtained by each nearest neighbor graph by using a k-means clustering algorithm. According to the method, a robust loss function is adopted to measure a reconstruction error therein, mark data is not used in the robust loss function for judgment, and after a semi-supervised method of non-negative matrix factorization is introduced, the efficiency and the accuracy rate can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a non-negative matrix decomposition clustering method based on graph regularization. Background technique [0002] In recent years, high-dimensional data has appeared in many fields, such as multimedia analysis, computer vision, pattern recognition, etc. How to perform dimensionality reduction operations has become a research topic. As a commonly used dimensionality reduction method, non-negative matrix factorization aims to learn local feature representation, although it provides a large number of problem formation techniques and algorithm methods for clustering problems, and is widely used in various applications. However, this dimensionality reduction method cannot guarantee good clustering performance, so non-negative matrix variables are proposed. The basic idea of ​​non-negative matrix factorization is to learn two non-negative matrices closest to the original mat...

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Problems solved by technology

[0004] In view of the above problems, the present invention provides a graph regularization-based robust structure non-negative matrix decomposition clustering method, which effectively solves the problem that the data in the prior art is polluted by noise and outliers, which may affect the block diagonal Discovery of structures and technical issues reducing their performance

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