Abstract

Feature selection for clustering is a challenging problem due to the absence of class labels. Existing approaches can select a feature subset to maintain clustering performance while reducing dimensionality. However, we are faced with two problems: (1) there could be many sets of features that seem equally good, and (2) these features are sensitive to small data perturbation, or the selection instability problem. In this work, we investigate the stability problem in feature selection for clustering. To the best of our knowledge, this is the first work that aims to improve the stability of feature selection algorithms for clustering. The importance comes from the fact that stable selection provides consistent meaning for clusters. In this paper, we first formally define the problem and propose a Local Singular Value Decomposition (LSVD) framework for stable and accurate feature selection. Empirical results on various data sets show that the proposed framework can significantly improve selection stability whilst maintaining the clustering performance comparing to the baseline methods. An additional advantage of this approach is that the selected features preserve the physical meaning of the original features, a desirable property for subsequent data analysis.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE 16th International Conference on Information Reuse and Integration, IRI 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages236-243
Number of pages8
ISBN (Print)9781467366564
DOIs
StatePublished - Oct 19 2015
Event16th IEEE International Conference on Information Reuse and Integration, IRI 2015 - San Francisco, United States
Duration: Aug 13 2015Aug 15 2015

Other

Other16th IEEE International Conference on Information Reuse and Integration, IRI 2015
CountryUnited States
CitySan Francisco
Period8/13/158/15/15

Keywords

  • Clustering
  • Feature Selection
  • Singular Value Decomposition
  • Stability

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Electrical and Electronic Engineering

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  • Cite this

    Alelyani, S., & Liu, H. (2015). A Local SVD Framework for Stable Feature Selection for Clustering. In Proceedings - 2015 IEEE 16th International Conference on Information Reuse and Integration, IRI 2015 (pp. 236-243). [7300983] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IRI.2015.47