Feature selection for clustering - a filter solution

By Manoranjan Dash, Ki-Hoon Choi, Peter Scheuermann, Huan Liu. IEEE International Conference on Data Mining. Proceedings, 2002

448 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9cdff0185ff9c37e8120c35dd3ca3a1483ee6a35

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In Computer Vision and Pattern Recognition

Citations in the corpus

Selection of time instants and intervals with Support Vector Regression for multivariate functional data (2020, background): “Variable selection has also been studied in other domains such as clustering (Dash et al., 2002; Dash and Liu, 2000; Dash et al., 1997; Law et al., 2004, 2003; Li et al., 2008) and supervised classiﬁcation (Aytug, 2015; Benítez-Peña et al., 2019; Bertolazzi et al., 2016; Carrizosa et al., 2011;…”
Feature Selection for Clustering: A Review (2018, methodology): “Other examples of filter model criteria used in feature selection for clustering include: feature dependency [62], entropy-based distance [15], and laplacian score [26, 80].”
A Graphbased Framework for Multi-Task Multi-View Learning (2011, background): “In particularly, between Steps (9) and (17), for the k th view, we collectively update the functions f ik for the tasks with this view.”
Efficient Feature Selection via Analysis of Relevance and Redundancy (2004, background): “According to the availability of class labels, there are feature selection meth-ods for supervised learning (Dash and Liu, 1997; Yu and Liu, 2003) as well as for unsupervised learning (Kim et al., 2000; Dash et al., 2002).”
Feature subset selection in large dimensionality domains (2010, background): “Hence feature selection is an active area of research in pattern recog23 nition [2], machine learning [3], data mining [4] and statistics [5].”
Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection (2014, methodology): “Most unsupervised feature selection approaches are either based on filters [13], [14], [15], wrappers [4], [16] or embedding [17], [18], [19].”
Variable Selection for Clustering with Gaussian Mixture Models (2009, methodology): “The so-called “ﬁlter” approaches select the variables before a clustering analysis (see, for instance, Dash et al., 2002; Jouve and Nicoloyannis, 2005).”
A review of unsupervised feature selection methods (2019, methodology): “Finally in Dash et al. (2002) amultivariate information-basedmethod similar to Dash et al. (1997) was introduced.”
Feature Selection: A literature Review (2014, background): “Information MDLM [49], CARDIE [50], DTM [51], Koller’ [52], FG [11], FCBF [53], BSE [17] Dash’s [54], SBUD [55],”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, methodology): “Dimensionality reduction techniques have been successfully applied to this area—both those that destroy data semantics and those that preserve them (feature selectors) [68,197].”
Subspace clustering for high dimensional data: a review (2004, methodology): “Entropy measurements are the basis of the filter model approach presented in [19; 22].”