Log-Sum Regularized Kaczmarz Algorithms for High-Order Tensor Recovery
arXiv (Cornell University)(2023)
摘要
Sparse and low rank tensor recovery has emerged as a significant area of
research with applications in many fields such as computer vision. However,
minimizing the $\ell_0$-norm of a vector or the rank of a matrix is NP-hard.
Instead, their convex relaxed versions are typically adopted in practice due to
the computational efficiency, e.g., log-sum penalty. In this work, we propose
novel log-sum regularized Kaczmarz algorithms for recovering high-order tensors
with either sparse or low-rank structures. We present block variants along with
convergence analysis of the proposed algorithms. Numerical experiments on
synthetic and real-world data sets demonstrate the effectiveness of the
proposed methods.
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关键词
kaczmarz algorithms,regularized,log-sum,high-order
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