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Differential-geometric Newton method for the best rank-(R1, R2, R3) approximation of tensors.

, , , and . Numer. Algorithms, 51 (2): 179-194 (2009)

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Decoupling Multivariate Functions Using Second-Order Information and Tensors., , and . LVA/ICA, volume 10891 of Lecture Notes in Computer Science, page 79-88. Springer, (2018)Differential-geometric Newton method for the best rank-(R1, R2, R3) approximation of tensors., , , and . Numer. Algorithms, 51 (2): 179-194 (2009)Unfolding Latent Tree Structures using 4th Order Tensors., , and . ICML (3), volume 28 of JMLR Workshop and Conference Proceedings, page 316-324. JMLR.org, (2013)Bounded Matrix Low Rank Approximation., , and . ICDM, page 319-328. IEEE Computer Society, (2012)A Spectral Algorithm for Latent Junction Trees., , , , and . UAI, page 675-684. AUAI Press, (2012)Compressing Neural Networks with Two-Layer Decoupling., , , and . CAMSAP, page 226-230. IEEE, (2023)Solving Systems of Polynomial Equations - A Tensor Approach., and . LSSC, volume 13127 of Lecture Notes in Computer Science, page 333-341. Springer, (2021)Jacobi Algorithm for the Best Low Multilinear Rank Approximation of Symmetric Tensors., , and . SIAM J. Matrix Anal. Appl., 34 (2): 651-672 (2013)Nonlinear system identification: Finding structure in nonlinear black-box models., , , and . CAMSAP, page 1-4. IEEE, (2017)Factorization Approach to Structured Low-Rank Approximation with Applications., , and . SIAM J. Matrix Anal. Appl., 35 (3): 1180-1204 (2014)