Discriminative sparsity preserving graph embedding

Jianping Gou, Lan Du, Keyang Cheng, Yingfeng Cai

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

    6 Citations (Scopus)

    Abstract

    In this paper, we propose a new dimensionality reduction method called discriminative sparsity preserving graph embedding (DSPGE). Unlike many existing graph embedding methods such as locality preserving projections (LPP) and sparsity preserving projections (SPP), the aim of DSPGE is to preserve the sparse reconstructive relationships of data while simultaneously capture the geometric and discriminant structure of data in the embedding space. Through the sparse reconstruction and class-specific adjacent graphs, DSPGE characterizes the intra-class and inter-class sparsity preserving scatters, seeking to achieve the optimal projections that simultaneously maximize the inter-class sparsity preserving scatter and minimize intra-class sparsity preserving scatter. The effectiveness of the proposed DSPGE is demonstrated on two popular face databases, compared to up-to-date methods. The experimental results show that DSPGE outperforms the competing methods with the satisfactory classification performance.

    Original languageEnglish
    Title of host publication2016 IEEE Congress on Evolutionary Computation (CEC 2016)
    Subtitle of host publicationVancouver, British Columbia, Canada, 24-29 July 2016, [Proceedings]
    Place of PublicationPiscataway, NJ
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages4250-4257
    Number of pages8
    ISBN (Electronic)9781509006229, 9781509006236
    ISBN (Print)9781509006243
    DOIs
    Publication statusPublished - 14 Nov 2016
    EventIEEE Congress on Evolutionary Computation 2016 - Vancouver Convention Centre, Vancouver, Canada
    Duration: 24 Jul 201629 Jul 2016
    http://www.wcci2016.org/index.php
    https://ieeexplore.ieee.org/xpl/conhome/7636124/proceeding (Proceedings)

    Conference

    ConferenceIEEE Congress on Evolutionary Computation 2016
    Abbreviated titleIEEE CEC 2016
    CountryCanada
    CityVancouver
    Period24/07/1629/07/16
    Internet address

    Keywords

    • Dimensionality reduction
    • Face recognition
    • Graph embedding
    • Sparse representation

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