Multi-view data subspace clustering method based on diversity and consistency constraints

Abstract

The invention discloses a multi-view data subspace clustering method based on diversity and consistency constraints. The multi-view data subspace clustering method comprises a step 1 of obtaining the diversity constraint of multi-view data subspace clustering; a step 2 of obtaining the consistency constraint of multi-view data subspace clustering; and a step 3 of obtaining a multi-view data subspace clustering model and solving the model. According to the technical scheme, multi-view data information is fully utilized, and the image clustering performance is improved.

Description

technical field [0001] The invention belongs to the field of image classification, in particular to a subspace clustering method of multi-view data based on difference and consistency constraints. Background technique [0002] With the development of science and technology, there are more and more ways to obtain data. A large amount of text, image, video and audio data fills all aspects of people's lives. The analysis and processing of large-scale data occupies an increasingly important role in the field of scientific research. more important position. The increasingly complex data content leads to an increase in data dimensions, and many data can be observed from different perspectives or described by multiple features. For example, video surveillance can capture the information of the same place from different angles, and images can be described by different features (such as gray degree value features, texture features, spatial relationship features, etc.). In fact, the...

AI Technical Summary

Problems solved by technology

However, the existing multi-view data clustering methods usually only consider the constraints of the difference or consistency of multi-view data information, and do not make full use of the redundancy and difference information of multi-view data.

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