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Factor Matrix Trace Norm Minimization for Low-Rank Tensor Completion
| Content Provider | CiteSeerX |
|---|---|
| Author | Liu, Yuanyuan Shangy, Fanhua Cheng, Hong Chengy, James Tongz, Hanghang |
| Abstract | Most existing low-n-rank minimization algorithms for ten-sor completion suffer from high computational cost due to involving multiple singular value decompositions (SVDs) at each iteration. To address this issue, we propose a novel fac-tor matrix trace norm minimization method for tensor com-pletion problems. Based on the CANDECOMP/PARAFAC (CP) decomposition, we first formulate a factor matrix rank minimization model by deducing the relation between the rank of each factor matrix and the mode-n rank of a ten-sor. Then, we introduce a tractable relaxation of our rank function, which leads to a convex combination problem of much smaller scale matrix nuclear norm minimization. Fi-nally, we develop an efficient alternating direction method of multipliers (ADMM) scheme to solve the proposed prob-lem. Experimental results on both synthetic and real-world data validate the effectiveness of our approach. Moreover, our method is significantly faster than the state-of-the-art approaches and scales well to handle large datasets. 1 |
| File Format | |
| Access Restriction | Open |
| Content Type | Text |
