Scalable deep k-subspace clustering

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

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

4 Citations (Scopus)


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
Subtitle of host publication14th Asian Conference on Computer Vision Perth, Australia, December 2–6, 2018 Revised Selected Papers, Part V
EditorsC.V. Jawahar, Hongdong Li, Greg Mori, Konrad Schindler
Place of PublicationCham Switzerland
Number of pages16
ISBN (Electronic)9783030208738
ISBN (Print)9783030208721
Publication statusPublished - 2019
EventAsian Conference on Computer Vision 2018 - Perth, Australia
Duration: 2 Dec 20186 Dec 2018
Conference number: 14th (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceAsian Conference on Computer Vision 2018
Abbreviated titleACCV 2018
Internet address


  • Deep learning
  • Scalable
  • Subspace clustering

Cite this