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*Corresponding author: Xi-Le Zhao.Ting-Wei Zhou, Xi-Le Zhao, Sheng Liu, and Wei-Hao Wu are with the School of Mathematical Sciences/Research Center for Image and Vision Computing, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China (e-mail: zhoutingwei2021@163.com; xlzhao122003@163.com; liusheng16@163.com; weihaowu99@163.com). Yu-Bang Zheng is with the School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China (e-mail: zhengyubang@163.com)Deyu Meng is with the School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi鈥檃n Jiaotong University, Xi鈥檃n, Shaanxi 710049, China, and also with Pazhou Laboratory (Huangpu), Guangzhou, Guangdong 510335, China (e-mail: dymeng@mail.xjtu.edu.cn)
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Stop being limited by fixed tensor decomposition families: TenExp dynamically mixes and matches decompositions to better capture low-rank data structures.
