Data subspace clustering method based on multiple view angles

Abstract

The invention discloses a data subspace clustering method based on multiple view angles, which comprises the steps of extracting multi-view-angle characteristics in a multi-view-angle database; for the multi-view-angle database, selecting a specific linear reconstruction expression method and determining a regularization constraint method corresponding to the linear reconstruction expression method; determining reconstruction error weight of each view angle characteristic in multi-view-angle characteristics; according to the selected reconstruction expression method and the obtained reconstruction error weights of different view angle characteristics, learning to obtain a linear expression matrix for reconstructing all samples in the multi-view-angle database, wherein the linear expression matrices are used for expressing a relationship among the samples in the database and element values are used for expressing reconstruction coefficients for corresponding samples in the line to reconstruct corresponding samples in the row; correspondingly processing the linear expression matrix to obtain an affinity matrix for measuring the similarity of the samples in the multi-view-angle database; and using a spectral clustering algorithm to partition the affinity matrix to obtain multi-view-angle data subspaces.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a data subspace clustering method based on multiple views. Background technique [0002] Data in reality often have multiple perspectives. For example, web page data includes both picture information and text information; video data includes audio information and picture information at the same time. The fundamental task of multi-view learning is to utilize the complementary information between different views to improve the performance of learning. Multi-view clustering is a basic task of multi-view learning. Traditional multi-view clustering methods are mostly based on spectral clustering, and Euclidean distance is the main method for measuring the similarity of sample points represented by different views. However, the eigen representations of the sample points in the data are often in different subspaces, and the Euclidean distance of the sample points under the...

AI Technical Summary

Problems solved by technology

However, the eigen representations of the sample points in the data are often in different subspaces, and the Euclidean distance of the sample points under the high-dimensional representation cannot effectively reflect the structural information of the data.

Therefore, traditional multi-view clustering cannot effectively cluster multi-view subspaces.

Images