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

A4 Article in conference proceedings

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

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