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The problem of maximum-likelihood learning of the structure of an unknown discrete distribution from samples is considered when the distribution is Markov on a tree. Large-deviation analysis of the error in estimation of the set of edges of the tree is performed. Necessary and sufficient conditions are provided to ensure that this error probability decays exponentially. These conditions are based...
This paper contributes to the field of N-way (N ges 3) tensor decompositions, which are increasingly popular in various signal processing applications. A novel PARATREE decomposition structure is introduced, accompanied with sequential unfolding SVD (SUSVD) algorithm. SUSVD applies a matrix SVD sequentially on the unfolded tensor, which is reshaped from the right hand basis vectors of the SVD of the...
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