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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 7/2019

20-06-2018 | Original Article

Joint sparse representation and locality preserving projection for feature extraction

Authors: Wei Zhang, Peipei Kang, Xiaozhao Fang, Luyao Teng, Na Han

Published in: International Journal of Machine Learning and Cybernetics | Issue 7/2019

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Abstract

Traditional graph-based feature extraction methods use two separated procedures, i.e., graph learning and projection learning to perform feature extraction. They make the feature extraction result highly dependent on the quality of the initial fixed graph, while the graph may not be the optimal one for feature extraction. In this paper, we propose a novel unsupervised feature extraction method, i.e., joint sparse representation and locality preserving projection (JSRLPP), in which the graph construction and feature extraction are simultaneously carried out. Specifically, we adaptively learn the similarity matrix by sparse representation, and at the same time, learn the projection matrix by preserving local structure. Compared with traditional feature extraction methods, our approach unifies graph learning and projection learning to a common framework, thus learns a more suitable graph for feature extraction. Experiments on several public image data sets demonstrate the effectiveness of our proposed algorithm.
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Metadata
Title
Joint sparse representation and locality preserving projection for feature extraction
Authors
Wei Zhang
Peipei Kang
Xiaozhao Fang
Luyao Teng
Na Han
Publication date
20-06-2018
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 7/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0849-y

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