A machine learning method for locally missing multi-view clustering based on matrix-guided regularization

Abstract

The invention relates to a machine learning method for locally missing multi-view clustering based on matrix-guided regularization.The method fuses filling and clustering, fills missing kernel under the guidance of clustering, clusters with filled kernel, and introduces matrix-guided regularization when filling missing kernel. The method comprises the following steps: 1) obtaining target data samples and clustering target numbers, mapping the target data samples to multi-kernel space; 2) introducing matrix-guided regularization to establish regularized locally missing multi-kernel k-means clustering optimization objective function; 3) solving the regularized locally missing multi-kernel k-means clustering optimization objective function in a cyclic manner to realize clustering. Compared with the prior art, the present invention has the advantages of good clustering effect, low calculation amount 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 local missing multi-view clustering machine learning method based on matrix-guided regularization. Background technique [0002] Multi-view clustering (MKC), which aims to optimally combine a set of pre-specified basic views for clustering, has been intensively studied in the past decades. For example, the pioneering work of "Multiple kernel clustering" (B.Zhao, J.T.Kwok, and C.Zhang, in SDM, 2009, pp.638–649) proposed a multi-kernel clustering algorithm that can maximize the optimization of hyperplane The maximum margin, the best cluster labels and the best kernel for . In the literature "Optimized data fusion for kernel k-means clustering" (S. Yu, L.-C. Tranchevent, X. Liu, W. J.A.K.Suykens, B.D.Moor, and Y.Moreau, IEEE TPAMI, vol.34, no.5, pp.1031–1039, 2012) proposed a novel optimized kernel k...

AI Technical Summary

Problems solved by technology

However, the above methods still have the following deficiencies: 1) Forcibly force closer and farther sample pairs to be equal to the same ideal similarity, and inappropriately ignore the variation of samples in the same category; 2) The multi-kernel matrix is ​​not fully considered , which may lead to high redundancy and low diversity in the chosen kernel

These two factors make these predefined kernel matrices not effectively utilized, which in turn adversely affects the clustering performance

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