We consider a decomposition method for compressive streaming data in the context of online compressive Robust Principle Component Analysis (RPCA). The proposed decomposition solves an n-ℓ1 cluster-weighted minimization to decompose a sequence of frames (or vectors), into sparse and low-rank components from compressive measurements. Our method processes a data vector of the stream per time instance from a small number of measurements in contrast to conventional batch RPCA, which needs to access full data. The n-ℓ1 cluster-weighted minimization leverages the sparse components along with their correlations with multiple previously-recovered sparse vectors. Moreover, the proposed minimization can exploit the structures of sparse components via clustering and re-weighting iteratively. The method outperforms the existing methods for both numerical data and actual video data.
|Title of host publication||Proceedings of 2018 Data Compression Conference|
|Publication status||Published - 2018|
|Event||2018 Data Compression Conference - Snowbird, United States|
Duration: 27 Mar 2018 → 30 Mar 2018
|Conference||2018 Data Compression Conference|
|Period||27/03/2018 → 30/03/2018|
Luong, H. V., Deligiannis, N., Forchhammer, S., & Kaup, A. (2018). Online Decomposition of Compressive Streaming Data Using n-ℓ1 Cluster-Weighted Minimization. In Proceedings of 2018 Data Compression Conference (pp. 62-9). IEEE. https://doi.org/10.1109/DCC.2018.00014