Unsupervised feature selection using orthogonal encoder-decoder factorization

Unsupervised feature selection (UFS) is a fundamental task in machine learning and data analysis, aimed at identifying a subset of non-redundant and relevant features from a high-dimensional dataset. Embedded methods seamlessly integrate feature selection into model training, resulting in more effic...

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Veröffentlicht in:	Information sciences Jg. 663; S. 120277
Hauptverfasser:	Mozafari, Maryam, Seyedi, Seyed Amjad, Pir Mohammadiani, Rojiar, Akhlaghian Tab, Fardin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.03.2024
Schlagworte:	Encoder-decoder model Nonnegative matrix factorization Pseudo-supervised learning Self-representation learning Unsupervised feature selection Pseudo-supervised learning Self-representation learning Unsupervised feature selection Encoder-decoder model Nonnegative matrix factorization
ISSN:	0020-0255, 1872-6291
Online-Zugang:	Volltext
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