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, Volume 21, Issue 6, pp 1745–1758 | Cite as

Joint self-representation and subspace learning for unsupervised feature selection

  • Ruili WangEmail author
  • Ming Zong
Article
Part of the following topical collections:
  1. Special Issue on Deep Mining Big Social Data

Abstract

This paper proposes a novel unsupervised feature selection method by jointing self-representation and subspace learning. In this method, we adopt the idea of self-representation and use all the features to represent each feature. A Frobenius norm regularization is used for feature selection since it can overcome the over-fitting problem. The Locality Preserving Projection (LPP) is used as a regularization term as it can maintain the local adjacent relations between data when performing feature space transformation. Further, a low-rank constraint is also introduced to find the effective low-dimensional structures of the data, which can reduce the redundancy. Experimental results on real-world datasets verify that the proposed method can select the most discriminative features and outperform the state-of-the-art unsupervised feature selection methods in terms of classification accuracy, standard deviation, and coefficient of variation.

Keywords

Unsupervised feature selection Self-representation Subspace learning 

Notes

Acknowledgments

This work was in part supported by the Marsden Fund of New Zealand and the China Scholarship Council.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Institute of Natural and Mathematical Sciences (INMS)Massey UniversityAucklandNew Zealand

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