Sparse subspace clustering for evolving data streams
Sui, Jinping; Liu, Zhen; Liu, Li; Jung, Alexander; Liu, Tianpeng; Peng, Bo; Li, Xiang (2019-04-17)
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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https://urn.fi/URN:NBN:fi-fe202003249094
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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.
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