Results 101 to 110 of about 1,665,186 (233)
Semilinear tensor decompositions
Journal of Computational AlgebraThis paper studies semilinear tensor decompositions of \(kG\)-modules, where \(G\) is a finite group and \(k\) is a field. The authors focus on \(kG\)-modules that admit a semilinear tensor decomposition, proving that this occurs if and only if the endomorphism algebra of the module contains a pair of mutually centralizing, \(G\)-invariant subalgebras ...
K.K. Mahavadi, A.J.E. Ryba
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Co-PARAFAC: A Novel Cost-Efficient Scalable Tensor Decomposition Algorithm
IEEE AccessThis paper proposes a novel tensor decomposition method, cooperative parallel factor (Co-PARAFAC), that is devised to achieve higher accuracy with lower computational complexity and memory requirements than the conventional PARAFAC.
Farshad Shams +2 more
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The average condition number of most tensor rank decomposition problems is infinite
, 2019 The tensor rank decomposition, or canonical polyadic decomposition, is the decomposition of a tensor into a sum of rank-1 tensors. The condition number of the tensor rank decomposition measures the sensitivity of the rank-1 summands with respect to ...
Beltrán, Carlos +2 more
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, 2016 Tensor networks have in recent years emerged as the powerful tools for solving the large-scale optimization problems. One of the most popular tensor network is tensor train (TT) decomposition that acts as the building blocks for the complicated tensor networks.
Zhao, Qibin +4 more
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Robust Image Hashing with Tensor Decomposition
IEEE Transactions on Knowledge and Data Engineering, 2019 This paper presents a new image hashing that is designed with tensor decomposition (TD), referred to as TD hashing, where image hash generation is viewed as deriving a compact representation from a tensor.
Zhenjun Tang +3 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThe structure information of hyperspectral image (HSI) is well-characterized by tensors, surpassing the capabilities of traditional compressive sensing reconstruction models based on vectors and matrices.
Xinwei Wan +4 more
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An Alternating Bayesian Approach to PARAFAC Decomposition of Tensors
IEEE Access, 2018 The PARAllel FACtor (PARAFAC) decomposition is known as one of the most commonly used tools in tensor signal/data processing. Unfortunately, its classical algorithms barely take the potential statistical and/or deterministic prior information of the ...
Ming Shi, Dan Li, Jian Qiu Zhang
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Hyperspectral Image Denoising With Group Sparse and Low-Rank Tensor Decomposition
IEEE Access, 2018 Hyperspectral image (HSI) is usually corrupted by various types of noise, including Gaussian noise, impulse noise, stripes, deadlines, and so on. Recently, sparse and low-rank matrix decomposition (SLRMD) has demonstrated to be an effective tool in HSI ...
Zhihong Huang +4 more
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Objects features extraction by singular projections of data tensor to matrices
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie ŚrodowiskaThe problem of multidimensional tensor objects features extraction in a manner of matrices is considered. The tensor’ elements Higher Order Singular Value Decomposition (SVD) is presented as the d-SVD which includes SVD of the tensor reshaped as a ...
Yuriy Bunyak +3 more
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Iterative Methods for Symmetric Outer Product Tensor Decompositions [PDF]
, 2013 We study the symmetric outer product decomposition which decomposes a fully (partially) symmetric tensor into a sum of rank-one fully (partially) symmetric tensors. We present iterative algorithms for the third-order partially symmetric tensor and fourth-
Li, Na, Navasca, Carmeliza
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