Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.