Generalized Multi-view Unsupervised Feature Selection

Abstract

Although many unsupervised feature selection (UFS) methods have been proposed, most of them still suffer from the following limitations: (1) these methods are usually just applicable to single-view data, thus cannot well exploit the ubiquitous complementarity among multiple views; (2) most existing UFS methods model the correlation between cluster structure and data distribution in linear ways, thus more general correlations are difficult to explore. Therefore, we propose a novel unsupervised feature selection method, termed as generalized Multi-View Unsupervised Feature Selection (gMUFS), to simultaneously explore the complementarity of multiple views, and complex correlation between cluster structure and selected features as well. Specifically, a multi-view consensus pseudo label matrix is learned and, the most valuable features are selected by maximizing the dependence between the consensus cluster structure and selected features in kernel spaces with Hilbert Schmidt independence criterion (HSIC).