Sparse subspace clustering for evolving data streams

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.

Sui Jinping, Liu Zhen, Liu Li, Jung Alexander, Liu Tianpeng, Peng Bo, Li Xiang

Publication type:
A4 Article in conference proceedings

Place of publication:
44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings

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


Full citation:
J. Sui et al., “Sparse Subspace Clustering for Evolving Data Streams,” ICASSP 2019 – 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2019, pp. 7455-7459. doi: 10.1109/ICASSP.2019.8683205


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