Joint Structured Graph Learning and Unsupervised Feature Selection

Yong Peng, Leijie Zhang, Wanzeng Kong, Feiping Nie, Andrzej Cichocki

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Citation (Scopus)

    Abstract

    The central task in graph-based unsupervised feature selection (GUFS) depends on two folds, one is to accurately characterize the geometrical structure of the original feature space with a graph and the other is to make the selected features well preserve such intrinsic structure. Currently, most of the existing GUFS methods use a two-stage strategy which constructs graph first and then perform feature selection on this fixed graph. Since the performance of feature selection severely depends on the quality of graph, the selection results will be unsatisfactory if the given graph is of low-quality. To this end, we propose a joint graph learning and unsupervised feature selection (JGUFS) model in which the graph can be adjusted to adapt the feature selection process. The JGUFS objective function is optimized by an efficient iterative algorithm whose convergence and complexity are analyzed in detail. Experimental results on representative benchmark data sets demonstrate the improved performance of JGUFS in comparison with state-of-the-art methods and therefore we conclude that it is promising of allowing the feature selection process to change the data graph.

    Original languageEnglish
    Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3572-3576
    Number of pages5
    ISBN (Electronic)9781479981311
    DOIs
    Publication statusPublished - May 2019
    Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
    Duration: 12 May 201917 May 2019

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    Volume2019-May
    ISSN (Print)1520-6149

    Conference

    Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
    Country/TerritoryUnited Kingdom
    CityBrighton
    Period12/05/1917/05/19

    Keywords

    • clustering
    • joint learning
    • non-negativity
    • structured graph learning
    • Unsupervised feature selection

    Fingerprint

    Dive into the research topics of 'Joint Structured Graph Learning and Unsupervised Feature Selection'. Together they form a unique fingerprint.

    Cite this