Title: A locality constrained self-representation approach for unsupervised feature selection

Authors: Cuihua Wang; Shuyi Ma; Chao Bi; Miao Qi; Hui Sun; Yugen Yi

Addresses: School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai, 264209, China ' College of Computer Science and Information Technology, Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, 130117, China ' School of Mathematics and Statistics, Northeast Normal University, Changchun, 130117, China ' College of Computer Science and Information Technology, Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, 130117, China ' College of Humanities and Sciences, Northeast Normal University, Changchun, 130117, China ' College of Computer Science and Information Technology, Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, 130117, China; School of Mathematics and Statistics, Northeast Normal University, Changchun, 130117, China

Abstract: Recently, regularised self-representation (RSR) has been proposed as an efficient unsupervised feature selection algorithm. However, RSR only takes the self-representation ability of features into account, and neglects the locality structure preserving ability of features, which may degrade its performance. To overcome this limitation, a novel algorithm termed locality constrained regularised self-representation (LCRSR) is proposed in this paper. In our algorithm, a local scatter matrix is introduced to encode the locality geometric structure of high-dimensional data. Therefore, the locality information of the input database can be well preserved. Moreover, a simple yet efficient iterative update algorithm is developed to solve the proposed LCRSR. Extensive experiments are conducted on five publicly available databases (such as JAFFE, ORL, AR, COIL20 and SRBCT) to demonstrate the efficiency of the proposed algorithm. Experimental results show that LCRSR obtains better clustering performance than some other state-of-the-art approaches.

Keywords: unsupervised feature selection; self-representation; local structure; clustering.

DOI: 10.1504/IJCSE.2017.084680

International Journal of Computational Science and Engineering, 2017 Vol.14 No.4, pp.299 - 308

Received: 12 Oct 2015
Accepted: 05 Nov 2015
Published online: 21 Jun 2017 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article