A machine learn method of multi-view clustering with regularization derive from matrix norm

Abstract

The invention relates to a regularized multi-view clustering machine learning method derived from matrix norm. The specific steps of the method include: 1) obtaining clustering task and target data samples; 2) deriving a regularization term based on that obtain matrix norm of the clustering task; 3) deriving a regularization term based on that matrix norm, and establishing a regularization multi-view clustering optimization objective function; 4) the regularized multi-view clustering optimization objective function is solved in a cyclic way to realize clustering. Compared with the prior art, the invention reduces the redundancy of selected cores and increases the diversity of selected cores by measuring the correlation between each pair of cores, and has the advantages of improving the clustering effect and the like.

Description

technical field [0001] The invention belongs to the technical field of computer vision and pattern recognition, and relates to a multi-view clustering method, in particular to a multi-view clustering machine learning method of matrix norm derivation regularization. Background technique [0002] The goal of the clustering algorithm is to cluster a group of samples into k clusters, and the intra-class similarity of the samples is greater than the inter-class similarity. As one of the classic clustering algorithms, k-means is a direct and effective clustering algorithm, which mainly has two steps: (i) setting k initial points (such as k cluster centers) for a given sample; (ii) Update the assignment matrix by minimizing the sum of squares loss for a given starting point. Clustering can be achieved by iterating these two steps until convergence. The algorithm has been widely researched and expanded because of its simple concept, easy extension and high efficiency. Among them, ...

AI Technical Summary

Problems solved by technology

This will cause the following problems: 1) highly correlated kernels will be selected at the same time, which unnecessarily increases the number and information of clustering kernels used; is suppressed, which reduces the diversity of selected nuclei and affects the use of nuclei with complementary information

Both of these problems will make the pre-given cores unable to be effectively used, and ultimately reduce the clustering effect

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