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J-GLOBAL ID:201502295868270194   Reference number:15A0224112

Tensor Decomposition via Convex Optimization

凸最適化に基づくテンソル分解
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Volume: 24  Issue:Page: 160-167  Publication year: Dec. 25, 2014 
JST Material Number: L1191A  ISSN: 0917-2270  Document type: Article
Article type: 原著論文  Country of issue: Japan (JPN)  Language: JAPANESE (JA)
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Mathematical physics  ,  System optimization methods 
Reference (33):
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  • Boyd, S., Parikh, N., Chu, E., Peleato, B. and Eckstein. J., Distributed optimization and statistical learning via the alternating direction method of multipliers, Foundations and Trends® in Machine Learning, 3[1] (2011), 1-122,
  • Candès, E. J. and Tao, T., The power of convex relaxation: Near-optimal matrix completion, IEEE T. Inform. Theory, 56[5] (2010), 2053-2080.
  • Carroll, J. D. and Chang. J. J., Analysis of individual differences in multidimensional scaling via an n-way generalization of "Eckart-Young" decomposition, Psychometrika, 35[3] (1970), 283-319.
  • Chandrasekaran, V., Recht, B., Parrilo, P. A. and Willsky, A. S., The convex geometry of linear inverse problems, Foundations of Computational Mathematics, 12 [6] (2012) , 805-849.
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