Learnable Scaled Gradient Descent for Guaranteed Robust Tensor PCA

• URL: http://arxiv.org/abs/2501.04565v2
• Date: Fri, 17 Jan 2025 05:13:06 GMT
• Title: Learnable Scaled Gradient Descent for Guaranteed Robust Tensor PCA
• Authors: Lanlan Feng, Ce Zhu, Yipeng Liu, Saiprasad Ravishankar, Longxiu Huang,
• Abstract summary: We propose an efficient scaled gradient descent (SGD) approach within the t-SVD framework for the first time. We show that RTPCA-SGD achieves linear convergence to the true low-rank tensor at a constant rate, independent of the condition number.
• Score: 39.084456109467204
• License:
• Abstract: Robust tensor principal component analysis (RTPCA) aims to separate the low-rank and sparse components from multi-dimensional data, making it an essential technique in the signal processing and computer vision fields. Recently emerging tensor singular value decomposition (t-SVD) has gained considerable attention for its ability to better capture the low-rank structure of tensors compared to traditional matrix SVD. However, existing methods often rely on the computationally expensive tensor nuclear norm (TNN), which limits their scalability for real-world tensors. To address this issue, we explore an efficient scaled gradient descent (SGD) approach within the t-SVD framework for the first time, and propose the RTPCA-SGD method. Theoretically, we rigorously establish the recovery guarantees of RTPCA-SGD under mild assumptions, demonstrating that with appropriate parameter selection, it achieves linear convergence to the true low-rank tensor at a constant rate, independent of the condition number. To enhance its practical applicability, we further propose a learnable self-supervised deep unfolding model, which enables effective parameter learning. Numerical experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed methods while maintaining competitive computational efficiency, especially consuming less time than RTPCA-TNN.

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