Conference paper
Compressive Online Robust Principal Component Analysis with Multiple Prior Information
Online Robust Principle Component Analysis (RPCA) arises naturallyin time-varying signal decomposition problems such as videoforeground-background separation. We propose a compressive online RPCA algorithm that decomposes recursively a sequence of datavectors (e.g., frames) into sparse and low-rank components.
Unlike conventional batch RPCA, which processes all the data directly, our method considers a small set of measurements taken per data vector (frame). Moreover, our method incorporates multiple prior information signals, namely previous reconstructed frames, to improve these paration and thereafter, update the prior information for the next frame.
Using experiments on synthetic data, we evaluate the separation performance of the proposed algorithm. In addition, we apply the proposed algorithm to online video foreground and background separation from compressive measurements. The results show that the proposed method outperforms the existing methods.
Language: | English |
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Publisher: | IEEE |
Year: | 2017 |
Pages: | 1260-1264 |
Proceedings: | 5th IEEE Global Conference on Signal and Information Processing |
ISBN: | 150905989X , 1509059903 , 1509059911 , 9781509059898 , 9781509059904 and 9781509059911 |
Types: | Conference paper |
DOI: | 10.1109/GlobalSIP.2017.8309163 |
ORCIDs: | Forchhammer, Søren |
Matrix decomposition Minimization Online Robust Principle Component Analysis Principal component analysis Robustness Signal processing algorithms Sparse matrices Time measurement compressive measurements compressive online RPCA algorithm compressive online robust principal component analysis conventional batch RPCA data vector recursive decomposition low-rank components multiple prior information signals n-l\ minimization previous reconstructed frames principal component analysis robust PCA separation performance source separation synthetic data time-varying signal decomposition problems vectors video foreground-background separation video signal processing