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Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives

, , , , , , and . Foundations and Trends® in Machine Learning, 9 (6): 249--429 (2017)
DOI: 10.1561/2200000067

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Regularized Computation of Approximate Pseudoinverse of Large Matrices Using Low-Rank Tensor Train Decompositions., and . SIAM J. Matrix Anal. Appl., 37 (2): 598-623 (2016)Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives., , , , , , and . CoRR, (2017)Big Data Matrix Singular Value Decomposition Based on Low-Rank Tensor Train Decomposition., and . ISNN, volume 8866 of Lecture Notes in Computer Science, page 121-130. Springer, (2014)Nonnegative Tensor Train Decompositions for Multi-domain Feature Extraction and Clustering., , , and . ICONIP (3), volume 9949 of Lecture Notes in Computer Science, page 87-95. (2016)Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives, , , , , , and . Foundations and Trends® in Machine Learning, 9 (6): 249--429 (2017)Block Tensor Train Decomposition for Missing Value Imputation.. APSIPA, page 1338-1343. IEEE, (2018)Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions, , , , , and . Foundations and Trends® in Machine Learning, 9 (4-5): 249--429 (2016)Estimating a Few Extreme Singular Values and Vectors for Large-Scale Matrices in Tensor Train Format., and . SIAM J. Matrix Anal. Appl., 36 (3): 994-1014 (2015)