Abstract

Feature selection has been proven to be effective and efficient in preparing high-dimensional data for many mining and learning tasks. Features of real-world high-dimensional data such as words of documents, pixels of images and genes of microarray data, usually present inherent hierarchical structures. In a hierarchical structure, features could share certain properties. Such information has been exploited to help supervised feature selection but it is rarely investigated for unsupervised feature selection, which is challenging due to the lack of labels. Since real world data is often unlabeled, it is of practical importance to study the problem of feature selection with hierarchical structures in an unsupervised setting. In particular, we provide a principled method to exploit hierarchical structures of features and propose a novel framework HUFS, which utilizes the given hierarchical structures to help select features without labels. Experimental study on real-world datasets is conducted to assess the effectiveness of the proposed framework.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017
PublisherSociety for Industrial and Applied Mathematics Publications
Pages507-515
Number of pages9
ISBN (Electronic)9781611974874
StatePublished - 2017
Event17th SIAM International Conference on Data Mining, SDM 2017 - Houston, United States
Duration: Apr 27 2017Apr 29 2017

Other

Other17th SIAM International Conference on Data Mining, SDM 2017
CountryUnited States
CityHouston
Period4/27/174/29/17

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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  • Cite this

    Wang, S., Wang, Y., Tang, J., Aggarwal, C., Ranganath, S., & Liu, H. (2017). Exploiting hierarchical structures for unsupervised feature selection. In Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017 (pp. 507-515). Society for Industrial and Applied Mathematics Publications.