Scalable Deep k-Subspace Clustering

Tong Zhang*, Pan Ji, Mehrtash Harandi, Richard Hartley, Ian Reid

*Corresponding author for this work

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

    26 Citations (Scopus)

    Abstract

    Subspace clustering algorithms are notorious for their scalability issues because building and processing large affinity matrices are demanding. In this paper, we introduce a method that simultaneously learns an embedding space along subspaces within it to minimize a notion of reconstruction error, thus addressing the problem of subspace clustering in an end-to-end learning paradigm. To achieve our goal, we propose a scheme to update subspaces within a deep neural network. This in turn frees us from the need of having an affinity matrix to perform clustering. Unlike previous attempts, our method can easily scale up to large datasets, making it unique in the context of unsupervised learning with deep architectures. Our experiments show that our method significantly improves the clustering accuracy while enjoying cheaper memory footprints.

    Original languageEnglish
    Title of host publicationComputer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
    EditorsHongdong Li, Konrad Schindler, Greg Mori, C.V. Jawahar
    PublisherSpringer Verlag
    Pages466-481
    Number of pages16
    ISBN (Print)9783030208721
    DOIs
    Publication statusPublished - 2019
    Event14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia
    Duration: 2 Dec 20186 Dec 2018

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11365 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference14th Asian Conference on Computer Vision, ACCV 2018
    Country/TerritoryAustralia
    CityPerth
    Period2/12/186/12/18

    Fingerprint

    Dive into the research topics of 'Scalable Deep k-Subspace Clustering'. Together they form a unique fingerprint.

    Cite this