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• W2028273200 abstract "In recent years, low-rank tensor completion (LRTC) problems have received a significant amount of attention in computer vision, data mining, and signal processing. The existing trace norm minimization algorithms for iteratively solving LRTC problems involve multiple singular value decompositions of very large matrices at each iteration. Therefore, they suffer from high computational cost. In this paper, we propose a novel trace norm regularized CANDECOMP/PARAFAC decomposition (TNCP) method for simultaneous tensor decomposition and completion. 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 tensor. Then, we introduce a tractable relaxation of our rank function, and then achieve a convex combination problem of much smaller-scale matrix trace norm minimization. Finally, we develop an efficient algorithm based on alternating direction method of multipliers to solve our problem. The promising experimental results on synthetic and real-world data validate the effectiveness of our TNCP method. Moreover, TNCP is significantly faster than the state-of-the-art methods and scales to larger problems." @default.
• W2028273200 created "2016-06-24" @default.
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• W2028273200 date "2015-11-01" @default.
• W2028273200 modified "2023-10-17" @default.
• W2028273200 title "Trace Norm Regularized CANDECOMP/PARAFAC Decomposition With Missing Data" @default.
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• W2028273200 doi "https://doi.org/10.1109/tcyb.2014.2374695" @default.
• W2028273200 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26470059" @default.
• W2028273200 hasPublicationYear "2015" @default.
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