Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition

• URL: http://arxiv.org/abs/2108.10448v1
• Date: Mon, 23 Aug 2021 23:49:40 GMT
• Title: Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition
• Authors: HanQin Cai, Zehan Chao, Longxiu Huang, Deanna Needell
• Abstract summary: We study the problem of tensor subtraction principal component analysis (TRPCA), which aims to separate an underlying low-multi-rank tensor and an outlier from their sum. In work, we propose a fast non-linear decomposition algorithm, coined Robust CURCUR, for empirically sparse problems.
• Score: 8.821527277034336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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