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• W1626337535 abstract "Higher-order tensors are becoming prevalent in many scientific areas such as computer vision, social network analysis, data mining and neuroscience. Traditional tensor decomposition approaches face three major challenges: model selecting, gross corruptions and computational efficiency. To address these problems, we first propose a parallel trace norm regularized tensor decomposition method, and formulate it as a convex optimization problem. This method does not require the rank of each mode to be specified beforehand, and can automatically determine the number of factors in each mode through our optimization scheme. By considering the low-rank structure of the observed tensor, we analyze the equivalent relationship of the trace norm between a low-rank tensor and its core tensor. Then, we cast a non-convex tensor decomposition model into a weighted combination of multiple much smaller-scale matrix trace norm minimization. Finally, we develop two parallel alternating direction methods of multipliers (ADMM) to solve our problems. Experimental results verify that our regularized formulation is effective, and our methods are robust to noise or outliers." @default.
• W1626337535 created "2016-06-24" @default.
• W1626337535 creator A5016082884 @default.
• W1626337535 creator A5054464310 @default.
• W1626337535 creator A5067858777 @default.
• W1626337535 date "2014-07-05" @default.
• W1626337535 modified "2023-10-16" @default.
• W1626337535 title "Generalized Higher-Order Tensor Decomposition via Parallel ADMM" @default.
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• W1626337535 doi "https://doi.org/10.48550/arxiv.1407.1399" @default.
• W1626337535 hasPublicationYear "2014" @default.
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