Robust auto-weighted multi-view subspace clustering with common subspace representation matrix.

ABSTRACT: In many computer vision and machine learning applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the underlying subspaces and cluster data points correctly. However, traditional subspace clustering methods can only be applied on data from one source, and how to extend these methods and enable the extensions to combine information from various data sources has become a hot area of research. Previous multi-view subspace methods aim to learn multiple subspace representation matrices simultaneously and these learning task for different views are treated equally. After obtaining representation matrices, they stack up the learned representation matrices as the common underlying subspace structure. However, for many pro

SUBMITTER: Zhuge W 

PROVIDER: S-EPMC5441581 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature