Robust local and global regularization non-negative matrix factorization clustering method

Abstract

The invention relates to the technical field of data processing, in particular to a robust local and global regularized non-negative matrix factorization clustering method, which comprises the following steps of: acquiring an image clustering sample; constructing a nearest adjacency graph on the local scattering of the sample and introducing smooth regularization; using transformation to represent a global geometric structure of the space, and taking the global geometric structure as an additional principal component graph regularization item to be incorporated into an NMF algorithm; graph regularization term constraint is applied to the original NMF model through joint modeling, and the basis matrix is constrained by using LP smoothness constraint; in error measurement, correlation entropy is used to replace Euclidean norm, so that a robust local and global regularized non-negative matrix factorization objective function is obtained; iteration is carried out for preset times by using an iterative weighting method according to the target function, the variables U and V are updated, and robust local and global regularized non-negative matrix factorization is completed; and carrying out clustering analysis on the coefficient matrix by adopting a K-means clustering algorithm.

Description

technical field [0001] The invention relates to the technical field of data processing, specifically, a non-negative matrix decomposition clustering method with robust local and global regularization. Background technique [0002] With the development of computer technology, high-dimensional data has been applied in different fields, and people pay more and more attention to data dimensionality reduction. Dimensionality reduction has a wide range of applications. By converting a single image into a data set in a high-dimensional space through high-dimensionalization of a single image data, it can reveal the internal structure and information of multivariate data for subsequent tasks such as visualization, classification, and clustering. . [0003] Non-negative matrix factorization (NMF), as an effective dimension reduction method, has been frequently applied in the fields of pattern recognition, computer vision and information retrieval. The basic idea of ​​non-negative ma...

AI Technical Summary

Problems solved by technology

But many data in the real world contain Gaussian noise, non-Gaussian noise (such as in the process of measuring and collecting gene expression data) or outliers, and effectively dealing with noise or outliers is a tricky problem in practical applications

In this case, the performance of non-negative matrix factorization methods based on Euclidean norm will be greatly reduced

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