Feature Selection for Clustering: A Review

By Salem Alelyani, Jiliang Tang, Huan Liu. Data Clustering: Algorithms and Applications, 2018

423 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f1167e2e1fa07cdf432c10beb373e07efd6a5e58

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

A review of feature selection methods with applications (2015, methodology): “Spectral feature selection (SPEC) and Laplacian Score (LS) univariate, similarity classification, clustering [4]”
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction (2015, background): “Techniques that combine variable selection and clustering assist in finding dimensions that distinguish—rather than simply characterize—the clusters [11, 12].”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, background): “Even though there are a lot of review papers on gene selection in the literatures [4], [5], [6], [7], [8], but to the best of our knowledge, there is no detailed discussion on the methods and separates them in such three categories as in this paper.”
A review of unsupervised feature selection methods (2019, methodology): “In the same way as in supervised and semi-supervised feature selection, according to the strategy used for selecting features, Unsupervised Feature Selection methods can be divided into three main approaches (Alelyani et al. 2013; Dong and Liu 2018): • Filter methods select the most relevant feature”
Feature Selection for Classification: A Review (2014, background): “A comprehensive review about unsupervised feature selection can be found in [1].”
Feature Selection (2016, methodology): “Currently, there exist some other surveys that give a summarization of feature selection algorithms, such as those in Guyon and Elisseeff (2003), Alelyani et al. (2013), Chandrashekar and Sahin (2014), and Tang et al. (2014).”
Unsupervised Deep Embedding for Clustering Analysis (2015, background): “Clustering has been extensively studied in machine learning in terms of feature selection (Boutsidis et al., 2009; Liu & Yu, 2005; Alelyani et al., 2013), distance functions (Xing et al., 2002; Xiang et al., 2008), grouping methods (Mac-Queen et al., 1967; Von Luxburg, 2007; Li et al., 2004), and…”