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Added by
Ryota Tomioka
Publication date
2014
fields
Mathematical Statistics
and research's language is
English
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We show that the spectral norm of a random $n_1times n_2times cdots times n_K$ tensor (or higher-order array) scales as $Oleft(sqrt{(sum_{k=1}^{K}n_k)log(K)}right)$ under some sub-Gaussian assumption on the entries. The proof is based on a covering number argument. Since the spectral norm is dual to the tensor nuclear norm (the tightest convex relaxation of the set of rank one tensors), the bound implies that the convex relaxation yields sample complexity that is linear in (the sum of) the number of dimensions, which is much smaller than other recently proposed convex relaxations of tensor rank that use unfolding.
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