Learning a subspace for clustering via pattern shrinking

Authors:
Chenping Hou;Feiping Nie;Yuanyuan Jiao;Changshui Zhang;Yi Wu
Affiliations:
Department of Mathematics and Systems Science, National University of Defense Technology, Changsha 410073, China;Department of Automation, Tsinghua University, Beijing 100084, China;Department of Mathematics and Systems Science, National University of Defense Technology, Changsha 410073, China;Department of Automation, Tsinghua University, Beijing 100084, China;Department of Mathematics and Systems Science, National University of Defense Technology, Changsha 410073, China
Venue:
Information Processing and Management: an International Journal
Year:
2013

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Abstract

Clustering is a basic technique in information processing. Traditional clustering methods, however, are not suitable for high dimensional data. Thus, learning a subspace for clustering has emerged as an important research direction. Nevertheless, the meaningful data are often lying on a low dimensional manifold while existing subspace learning approaches cannot fully capture the nonlinear structures of hidden manifold. In this paper, we propose a novel subspace learning method that not only characterizes the linear and nonlinear structures of data, but also reflects the requirements of following clustering. Compared with other related approaches, the proposed method can derive a subspace that is more suitable for high dimensional data clustering. Promising experimental results on different kinds of data sets demonstrate the effectiveness of the proposed approach.