Online Decomposition of Compressive Streaming Data Using n-ℓ1 Cluster-Weighted Minimization

Huynh Van Luong, Nikos Deligiannis, Søren Forchhammer, Andre Kaup

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


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 languageEnglish
Title of host publicationProceedings of 2018 Data Compression Conference
Publication date2018
ISBN (Print)9781538648834
Publication statusPublished - 2018
Event2018 Data Compression Conference - Snowbird, United States
Duration: 27 Mar 201830 Mar 2018


Conference2018 Data Compression Conference
CountryUnited States

Cite this

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.