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11 245 (0,159s)

Result

Canonical polyadic tensor decomposition with low-rank factor matrices

This paper proposes a constrained canonical polyadic (CP) tensor decomposition method with low-rank factor matrices. In this way, we allow the CP decomposition with high rank while keeping the number of the model p...

Electrical and electronic engineering

  • 2021
  • D
  • Link
Result

Partitioned Alternating Least Squares Technique for Canonical Polyadic Tensor Decomposition

Canonical polyadic decomposition (CPD), also known as parallel factor analysis, is a representation of a given tensor as a sum of rank-one components. Traditional method for accomplishing CPD is the alternating least squar...

BB - Aplikovaná statistika, operační výzkum

  • 2016
  • Jx
  • Link
Result

Partitioned Hierarchical Alternating Least Squares Algorithm for CP Tensor Decomposition

Canonical polyadic decomposition (CPD), also known as PARAFAC, is a representation of a given tensor as a sum of rank-one tensors. Traditional method for accomplishing CPD is the alternating least squares (ALS) algorithm. T...

Statistics and probability

  • 2017
  • D
  • Link
Result

Numerical CP Decomposition of Some Difficult Tensors

In this paper, a numerical method is proposed for canonical polyadic (CP) decomposition of small size tensors. The focus is primarily on decomposition of tensors that correspond to small matrix multiplications. Her...

Applied mathematics

  • 2017
  • Jimp
  • Link
Result

Cramér-Rao-Induced Bounds for CANDECOMP/ PARAFAC Tensor Decomposition

estimates of factor matrices in Canonical Polyadic (CP) or CANDECOMP/PARAFAC (CP) decompositions of a tensor from noisy observations, (i.e., the tensor plus a random Gaussian decomposition algorithms, prediction o...

BB - Aplikovaná statistika, operační výzkum

  • 2013
  • Jx
  • Link
Result

Weighted Krylov-Levenberg-Marquardt method for canonical polyadic tensor decomposition

Weighted canonical polyadic (CP) tensor decomposition appears in a wide range of applications. A typical situation where the weighted decomposition is needed is when some-scale decomposition tasks are base...

Statistics and probability

  • 2020
  • D
  • Link
Result

Rank-one tensor injection: A novel method for canonical polyadic tensor decomposition

Canonical polyadic decomposition of tensor is to approximate or express of the tensor are highly collinear, the decomposition becomes difficult. Algorithms, e.g localminima. This paper proposes a novel method for s...

BB - Aplikovaná statistika, operační výzkum

  • 2016
  • D
Result

Non-orthogonal tensor diagonalization

of order-three or order-four tensors. The latter two algorithms may serve for canonical polyadic (CP) tensor decomposition, and in certain scenarios they can outperformntraditional CP decomposition methods. Finall...

Statistics and probability

  • 2017
  • Jimp
  • Link
Result

CANDECOMP/PARAFAC Decomposition of High-Order Tensors Through Tensor Reshaping

In general, algorithms for order-3 CANDECOMP/ PARAFAC (CP), also coined canonical polyadic decomposition (CPD), are easy to implement and can be extended to higher order CPD. Unfortunately, the algorithms become computation...

BB - Aplikovaná statistika, operační výzkum

  • 2013
  • Jx
  • Link
Result

Fast damped Gauss-Newton algorithm for nonnegative matrix factorization

Alternating optimization algorithms for canonical polyadic decomposition (with/without nonnegative constraints) often accompany update rules with low computational cost, but could face problems of swamps, bottlenecks, and s...

BB - Aplikovaná statistika, operační výzkum

  • 2011
  • D
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