FRPCA: Fast Robust Principal Component Analysis for online observations

While the performance of Robust Principal Component Analysis (RPCA), in terms of the recovered low-rank matrices, is quite satisfactory to many applications, the time efficiency is not, especially for scalable data. We propose to solve this problem using a novel fast incremental RPCA (FRPCA) approac...

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Bibliographic Details
Main Authors: Abdel-Hakim, Alaa E., El-Saban, M.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Complexity theory
Convex functions
Data models
Mathematical model
Principal component analysis
Robustness
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Summary:While the performance of Robust Principal Component Analysis (RPCA), in terms of the recovered low-rank matrices, is quite satisfactory to many applications, the time efficiency is not, especially for scalable data. We propose to solve this problem using a novel fast incremental RPCA (FRPCA) approach. The low rank matrices of the incrementally-observed data are estimated using a convex optimization model that exploits information obtained from the preestimated low-rank matrices of the original observations. The evaluation results supports the potential of FRPCA for fast, yet accurate, recovery of the low-rank matrices. The proposed FRPCA boosts the efficiency of the traditional RPCA by multiple hundreds of times, while scarifying less than 1% of accuracy.
ISSN:1051-4651
2831-7475