Multi-view subspace clustering method for self-weighted fusion of local and global information

Abstract

The invention provides a multi-view subspace clustering method for self-weighted fusion of local and global information. The method comprises the following steps: firstly, preprocessing acquired multi-view data; secondly, when local information of the original multi-view data is mined through graph learning, adding self-representation learning to mine global information of the original multi-view data, so that a high-quality and high-robustness similarity matrix is obtained; and then fusing the similarity matrixes of all views in a self-weighting mode to form a consistent similarity matrix. Rank constraint is introduced into the consistent similarity matrix, so that the number of connected components in the consistent similarity matrix is equal to the number of clustering clusters, and a multi-view clustering result is directly obtained. In this way, it is avoided that after the consistent similarity matrix is obtained, additional clustering steps need to be executed to obtain the clustering result. According to the method provided by the invention, the clustering structure of the data is disclosed while the consistent similarity matrix is generated, and an additional clustering step is not required to be executed to obtain a clustering result. Therefore, the method provided by the invention has a good clustering effect on the multi-view data.

Description

technical field [0001] The present invention relates to technical fields such as computer vision, pattern recognition and data mining, and more specifically relates to a multi-view subspace clustering method for self-weighted fusion of local and global information. Background technique [0002] With the rapid development of Internet technology, the means for people to obtain data are becoming more and more diverse, resulting in the continuous generation of a large amount of unlabeled data. Under the influence of the environment of today's big data era, how to analyze and process these unlabeled data so as to reveal its inherent laws has become a widely concerned issue in all walks of life. As an unsupervised learning technique, clustering is widely used in fields such as machine learning, computer vision, and data mining. Clustering attempts to divide the unlabeled data into several clusters according to the inherent characteristics of the data itself, that is, the "intra-c...

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Problems solved by technology

Poor performance of traditional clustering methods when single-view dat

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