Sparse Subspace Clustering for Evolving Data Streams

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


  • J. Sui
  • Z. Liu
  • L. Liu
  • Alex Jung

  • Tianpeng Liu
  • Bo Peng
  • Xiang Li

Research units

  • National University of Defense Technology
  • University of Oulu


The data streams arising in many applications can be modeled as a union of low-dimensional subspaces known as multi-subspace data streams (MSDSs). Clustering MSDSs according to their underlying low-dimensional subspaces is a challenging problem which has not been resolved satisfactorily by existing data stream clustering (DSC) algorithms. In this paper, we propose a sparse-based DSC algorithm, which we refer to as dynamic sparse subspace clustering (D-SSC). This algorithm recovers the low-dimensional subspaces (structures) of high-dimensional data streams and finds an explicit assignment of points to subspaces in an online manner. Moreover, as an online algorithm, D-SSC is able to cope with the time-varying structure of MSDSs. The effectiveness of D-SSC is evaluated using numerical experiments.


Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Publication statusPublished - 1 May 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom
Duration: 12 May 201917 May 2019
Conference number: 44

Publication series

NameIEEE International Conference on Acoustics Speech and Signal Processing
ISSN (Print)1520-6149


ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
CountryUnited Kingdom

    Research areas

  • Data stream clustering, high-dimensional data stream, subspace clustering, online clustering

ID: 34111591