Double feature selection algorithm based on low-rank sparse non-negative matrix factorization

Recently, many feature selection algorithms based on non-negative matrix factorization have been proposed. However, many of these algorithms only consider unilateral information about global or local geometric structure normally. To this end, this paper proposes a new feature selection algorithm cal...

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Bibliographic Details
Published in:	International journal of machine learning and cybernetics Vol. 11; no. 8; pp. 1891 - 1908
Main Authors:	Shang, Ronghua, Song, Jiuzheng, Jiao, Licheng, Li, Yangyang
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2020 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Complex Systems Computational Intelligence Control Datasets Engineering Factorization Feature selection Mechatronics Original Article Pattern Recognition Representations Robotics Sparsity Systems Biology Unsupervised feature selection Self-representation Non-negative matrix factorization Low-rank sparse representation
ISSN:	1868-8071, 1868-808X
Online Access:	Get full text
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