Abstract
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.
Original language | English |
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Title of host publication | Proceedings of 2018 Data Compression Conference |
Publisher | IEEE |
Publication date | 2018 |
Pages | 62-9 |
ISBN (Print) | 9781538648834 |
DOIs | |
Publication status | Published - 2018 |
Event | 2018 Data Compression Conference - Snowbird, United States Duration: 27 Mar 2018 → 30 Mar 2018 |
Conference
Conference | 2018 Data Compression Conference |
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Country/Territory | United States |
City | Snowbird |
Period | 27/03/2018 → 30/03/2018 |