Unsupervised feature extraction by low-rank and sparsity preserving embedding

Manifold based feature extraction has been proved to be an effective technique in dealing with the unsupervised classification tasks. However, most of the existing works cannot guarantee the global optimum of the learned projection, and they are sensitive to different noises. In addition, many metho...

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Bibliographic Details

Published in:	Neural networks Vol. 109; pp. 56 - 66
Main Authors:	Zhan, Shanhua, Wu, Jigang, Han, Na, Wen, Jie, Fang, Xiaozhao
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.01.2019
Subjects:	Algorithms Databases, Factual - classification Feature extraction Humans Low-rank representation Projection learning Sparse representation Unsupervised Machine Learning - classification Projection learning Feature extraction Sparse representation Low-rank representation
ISSN:	0893-6080, 1879-2782, 1879-2782
Online Access:	Get full text
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