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Erschienen in: World Wide Web 6/2018

06.11.2017

Joint self-representation and subspace learning for unsupervised feature selection

verfasst von: Ruili Wang, Ming Zong

Erschienen in: World Wide Web | Ausgabe 6/2018

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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.
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Fußnoten
2
UCI Repository of Machine Learning Datasets, http://​archive.​ics.​uci.​edu
 
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Metadaten
Titel
Joint self-representation and subspace learning for unsupervised feature selection
verfasst von
Ruili Wang
Ming Zong
Publikationsdatum
06.11.2017
Verlag
Springer US
Erschienen in
World Wide Web / Ausgabe 6/2018
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-017-0508-3

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