A review of unsupervised feature selection methods

Saúl Solorio-Fernández, J. Ariel Carrasco-Ochoa, José Fco Martínez-Trinidad

Research output: Contribution to journalArticleResearchpeer-review

Abstract

© 2019, Springer Nature B.V. In recent years, unsupervised feature selection methods have raised considerable interest in many research areas; this is mainly due to their ability to identify and select relevant features without needing class label information. In this paper, we provide a comprehensive and structured review of the most relevant and recent unsupervised feature selection methods reported in the literature. We present a taxonomy of these methods and describe the main characteristics and the fundamental ideas they are based on. Additionally, we summarized the advantages and disadvantages of the general lines in which we have categorized the methods analyzed in this review. Moreover, an experimental comparison among the most representative methods of each approach is also presented. Finally, we discuss some important open challenges in this research area.
Original languageAmerican English
JournalArtificial Intelligence Review
DOIs
StatePublished - 1 Jan 2019
Externally publishedYes

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Feature extraction
Taxonomies
Labels
taxonomy
Feature Selection
ability

Cite this

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title = "A review of unsupervised feature selection methods",
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A review of unsupervised feature selection methods. / Solorio-Fernández, Saúl; Carrasco-Ochoa, J. Ariel; Martínez-Trinidad, José Fco.

In: Artificial Intelligence Review, 01.01.2019.

Research output: Contribution to journalArticleResearchpeer-review

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