Tensors | Multilinear algebra

Tensor rank decomposition

In multilinear algebra, the tensor rank decomposition, the exact decomposition of a tensor in terms of the minimum terms, is an open problem. Canonical polyadic decomposition (CPD) is a variant of the rank decomposition which computes the best fitting terms for a user specified . The CP decomposition has found some applications in linguistics and chemometrics. The CP rank was introduced by Frank Lauren Hitchcock in 1927 and later rediscovered several times, notably in psychometrics. The CP decomposition is referred to as CANDECOMP, PARAFAC, or CANDECOMP/PARAFAC (CP). Another popular generalization of the matrix SVD known as the higher-order singular value decomposition computes orthonormal mode matrices and has found applications in statistics, signal processing, computer vision, computer graphics, psychometrics. (Wikipedia).

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