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Orthogonally decomposable tensors

I know nothing about orthogonally decomposable tensors, but they look at a glance to generalise your usual linear algebra in a way useful for the statistical inference of mixture models, while nonetheless being more computationally tractable than your garden variety tensor methods, which would be useful if it is indeed so.


Anandkumar, A., Ge, R., Hsu, D., Kakade, S. M., & Telgarsky, M. (2015) Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). In K. Chaudhuri, C. GENTILE, & S. Zilles (Eds.), Algorithmic Learning Theory (pp. 19–38). Springer International Publishing
Belkin, M., Rademacher, L., & Voss, J. (2016) Basis Learning as an Algorithmic Primitive. (pp. 446–487). Presented at the 29th Annual Conference on Learning Theory
Rabusseau, G., & Denis, F. (2014) Learning Negative Mixture Models by Tensor Decompositions. arXiv:1403.4224 [cs].
Robeva, E. (2016) Orthogonal Decomposition of Symmetric Tensors. SIAM Journal on Matrix Analysis and Applications, 37(1), 86–102. DOI.
Robeva, E., & Seigal, A. (2016) Singular Vectors of Orthogonally Decomposable Tensors. arXiv:1603.09004 [math].