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Showing 1 to 5 of 5 for “"canonical polyadic decomposition"”.
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New results in canonical polyadic decomposition overfinite fields
Canonical polyadic decomposition (CPD) consists of expressing a tensor (multidimensional array) as a sum of several rank-1 tensors, each of which is an outer/separable product of vectors. The number of rank-1 tensors used in a CPD is called the rank of the CPD, and the minimum possible rank of a …
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An Iterative Method for Canonical Polyadic Decomposition of Tensors
<p>The Singular Value Decomposition (SVD) of matrices is widely used in least-squares regression, image and data processing, principal component analysis and many other applications. Often only the larger singular values of the matrix are needed to reconstruct the matrix and those below a given …
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Parallel Algorithms for and Applications of the Dense Canonical Polyadic Decomposition
Tensor decompositions have gained popularity in various research communities as a means of analyzing high dimensional, complex data.
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Breaking the curse of dimensionality in electronic structure methods: towards optimal utilization of the canonical polyadic decomposition
… and application of higher-order tensor (HOT) decompositions, specifically the canonical polyadic (CP) decomposition, is fairly limited. The CP decomposition is an incredibly useful sparse tensor factorization that has the ability to disentangle all correlated modes of a tensor. However the …
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Constrained Matrix and Tensor Factorization: Theory, Algorithms, and Applications
… two-way to higher-way tensor data, the so-called canonical polyadic decomposition (CPD) model can provide essentially unique factors under mild conditions, but it is also widely accepted that if correct priors are imposed on the latent factors, the estimation performance can be greatly enhanced. …