Cluster structure preserving unsupervised feature selection for multi-view tasks

Multi-view or multi-modal tasks exist in many areas of pattern analysis as the advancement of feature acquisition or extraction. These tasks are usually confronted with the issue of curse of dimensionality. In this work we consider the unsupervised feature selection problem for multi-view tasks. As most of the existing feature selection methods can only handle single-view data, we develop a new algorithm, called Cluster Structure Preserving Unsupervised Feature Selection (CSP-UFS). To leverage the complementary information between multiple views in unsupervised scenarios, we incorporate discriminative analysis, spectral clustering and correlation information between multiple views into a unified framework. Intuitionally speaking, the cluster structures of data in feature spaces reflect the discriminative information of distinct classes. Thus we introduce spectral clustering to discover the cluster structure and use discriminative analysis to preserve the structure. We design an alternating optimization algorithm to solve the proposed objective function. Experimental results on different datasets show the effectiveness of the proposed algorithm.