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Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition

2021-08-23 23:49:40
HanQin Cai, Zehan Chao, Longxiu Huang, Deanna Needell

Abstract

We study the problem of tensor robust principal component analysis (TRPCA), which aims to separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their sum. In this work, we propose a fast non-convex algorithm, coined Robust Tensor CUR (RTCUR), for large-scale TRPCA problems. RTCUR considers a framework of alternating projections and utilizes the recently developed tensor Fiber CUR decomposition to dramatically lower the computational complexity. The performance advantage of RTCUR is empirically verified against the state-of-the-arts on the synthetic datasets and is further demonstrated on the real-world application such as color video background subtraction.

Abstract (translated)

URL

https://arxiv.org/abs/2108.10448

PDF

https://arxiv.org/pdf/2108.10448.pdf


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