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.
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Rabusseau, Guillaume, and François Denis. 2014. “Learning Negative Mixture Models by Tensor Decompositions,” March. http://arxiv.org/abs/1403.4224.
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