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Online Tensor Robust Principal Component Analysis
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DOI：10.1109/ACCESS.2022.3186364.png)
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摘要
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Online robust principal component analysis (RPCA) algorithms recursively decompose incoming data into low-rank and sparse components. However, they operate on data vectors and cannot directly be applied to higher-order data arrays (e.g. video frames). In this paper, we propose a new online robust PCA algorithm that preserves the multi-dimensional structure of data. Our algorithm is based on the recently proposed tensor singular value decomposition (T-SVD). We develop a convex optimization-based approach to recover the sparse component; and subsequently, update the low-rank component using incremental T-SVD. We propose an efficient tensor convolutional extension to the fast iterative shrinkage thresholding algorithm (FISTA) to produce a fast algorithm to solve this optimization problem. We demonstrate tensor-RPCA with the application of background foreground separation in a video stream. The foreground component is modeled as a sparse signal. The background component is modeled as a gradually changing low-rank subspace. Extensive experiments on real-world videos are presented and results demonstrate the effectiveness of our online tensor robust PCA.
Keyword:
Multilinear subspace learning
tensor convolutional sparse coding
low-rank tensor model
tensor singular value decomposition (T-SVD)
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA将PCA扩展到高阶数据结构: 张量，张量分解和张量PCA简介.png)
PROCEEDINGS OF THE IEEE
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PROCEEDINGS OF THE IEEEIF25.9
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