Sparse Subspace Clustering for Evolving Data Streams
Jinping Sui, Zhen Liu, Li Liu, Alexander Jung, Tianpeng Liu, Bo Peng, Xiang Li
Abstract
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.
BibTeX
@inproceedings{icassp2019_sparsesubspacecl,
title = {Sparse Subspace Clustering for Evolving Data Streams},
author = {Jinping Sui and Zhen Liu and Li Liu and Alexander Jung and Tianpeng Liu and Bo Peng and Xiang Li},
booktitle = {ICASSP 2019},
year = {2019}
}