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arXiv Machine Learning By Xingchen Xiao (School of Mathematics and Statistics, Southwest University, Chongqing, China), Feng Zhang (School of Mathematics and Statistics, Southwest University, Chongqing, China), Wenjin Qin (School of Mathematics and Statistics, Southwest University, Chongqing, China), Jianjun Wang (School of Mathematics and Statistics, Southwest University, Chongqing, China)

Semi-Tensor Product-Based Multi-Term Randomized T-SVD and Its Visual Applications

Read the original on arXiv Machine Learning →

The paper introduces a new semi‑tensor product for third‑order tensors that relaxes the dimensional constraints of the standard t‑product while preserving the closed‑form nature of T‑SVD. It builds a multi‑term semi‑tensor product singular value decomposition (MSTP‑SVD) that improves low‑rank approximation accuracy, and further accelerates it with randomized projection and power iteration to create the MRSTP‑SVD algorithm. Experiments on image and video compression and completion show that this method balances reconstruction accuracy and computational efficiency.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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