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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Vydané v:	International journal of machine learning and cybernetics Ročník 11; číslo 8; s. 1891 - 1908
Hlavní autori:	Shang, Ronghua, Song, Jiuzheng, Jiao, Licheng, Li, Yangyang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2020 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
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