Orthogonal and unitary tensor decomposition from an algebraic perspective

Author:

Boralevi Ada,Draisma Jan,Horobeţ Emil,Robeva Elina

Publisher

Springer Science and Business Media LLC

Subject

General Mathematics

Reference19 articles.

1. A. Anandkumar, R. Ge, D. Hsu, S. M. Kakade and M. Telegarski, Tensor decompositions for learning latent variable models, Journal of Machine Learning Research 15 (2014), 2773–2832.

2. A. Anandkumar, R. Ge and M. Janzamin, Sample complexity analysis for learning overcomplete latent variable models through tensor methods, Preprint, arXiv:1408.0553.

3. K. Batselier, H. Liu and N. Wong, A constructive algorithm for decomposing a tensor into a finite sum of orthonormal rank-1 terms, SIAM Journal on Matrix Analysis and Applications 36 (2015), 1315–1337.

4. P. Comon, Independent component analysis, a new concept?, Signal Processing 36 (1994), 287–314.

5. J. Chen and Y. Saad, On the tensor SVD and the optimal low rank orthogonal approximation of tensors, SIAM Journal on Matrix Analysis and Applications 30 (2009), 1709–1734.

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