Learning a Subspace for Clustering via Pattern Shrinking

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Authors Chenping Hou, Feiping Nie, Changshui Zhang, Yi Wu
Journal/Conference Name (IPM)
Paper Category
Paper 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.
Date of publication 2013
Code Programming Language MATLAB
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