title: Discriminative sparse subspace learning and its application to unsupervised feature selection.
abstract: In order to efficiently use the intrinsic data information, in this study a Discriminative Sparse Subspace Learning (DSSL) model has been investigated for unsupervised feature selection. First, the feature selection problem is formulated as a subspace learning problem. In order to efficiently learn the discriminative subspace, we investigate the discriminative information in the subspace learning process. Second, a two-step TDSSL algorithm and a joint modeling JDSSL algorithm are developed to incorporate the clusters׳ assignment as the discriminative information. Then, a convergence analysis of these two algorithms is provided. A kernelized discriminative sparse subspace learning (KDSSL) method is proposed to handle the nonlinear subspace learning problem. Finally, extensive experiments are conducted on real-world datasets to show the superiority of the proposed approaches over several state-of-the-art approaches.
authors.affiliation: Center for Robotics, School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China. Electronic address: nzhouuestc@gmail.com.
authors.forename: Nan
authors.initials: N
authors.lastname: Zhou
authors.affiliation: Center for Robotics, School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China. Electronic address: hcheng@uestc.edu.cn.
authors.forename: Hong
authors.initials: H
authors.lastname: Cheng
authors.affiliation: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6R 2V4, Canada; Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland. Electronic address: wpedrycz@ualberta.ca.
authors.forename: Witold
authors.initials: W
authors.lastname: Pedrycz
authors.affiliation: Center for Robotics, School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China. Electronic address: mathzy@uestc.edu.cn.
authors.forename: Yong
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Computer Science and Technology, Tsinghua University, Beijing, China. Electronic address: hpliu@tsinghua.edu.cn.
authors.forename: Huaping
authors.initials: H
authors.lastname: Liu
keywords: Feature selection; Kernel learning; Machine learning; Subspace learning; Unsupervised learning