We study robust PCA for the fully observed setting, which is about separating a low rank matrix L and a sparse matrix S from their sum D=L+S. In this talk, a new algorithm, dubbed accelerated alternating projections, is introduced for robust PCA which significantly improves the computational efficiency of the existing non-convex algorithms. Exact recovery guarantee has been established which shows linear convergence of the proposed algorithm. Empirical performance evaluations confirm the advantage of our algorithm over other state-of-the-art algorithms for robust PCA. Furthermore, we extend our method to the low-rank Hankel matrix, with its application to the spectrally sparse signals.
25 Mar 2020
3:00pm - 4:00pm
Where
https://hkust.zoom.com.cn/j/590198340
Speakers/Performers
Dr. HanQin CAI
University of California at Los Angeles
Organizer(S)
Department of Mathematics
Contact/Enquiries
mathseminar@ust.hk
Payment Details
Audience
Alumni, Faculty and Staff, PG Students, UG Students
Language(s)
English
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