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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Vydáno v:	Neural networks Ročník 109; s. 56 - 66
Hlavní autoři:	Zhan, Shanhua, Wu, Jigang, Han, Na, Wen, Jie, Fang, Xiaozhao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.01.2019
Témata:	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
On-line přístup:	Získat plný text
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