ONLINE TENSOR LOW-RANK REPRESENTATION FOR STREAMING DATA

This paper proposes a new streaming algorithm to learn low-rank structures of tensor data using the recently proposed tensor-tensor product (t-product) and tensor singular value decomposition (t-SVD) algebraic framework. It reformulates the tensor low-rank representation (TLRR) problem using the equivalent bifactor model of the tensor nuclear norm, where the tensor dictionary is updated based on the newly collected data and representations. Compared to TLRR, the proposed method processes tensor data in an online fashion and makes the memory cost independent of the data size. Experimental results on three benchmark datasets demonstrate the superior performance, efficiency and robustness of the proposed algorithm over state-of-the-art methods.