Results 21 to 30 of about 16,615 (264)
Symmetric tensor decomposition
Linear Algebra and its Applications, 2010 Publication in the conference proceedings of EUSIPCO, Glasgow, Scotland ...
Brachat, Jérôme +3 more
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Randomized CP tensor decomposition
Machine Learning: Science and Technology, 2020 Abstract The CANDECOMP/PARAFAC (CP) tensor decomposition is a popular dimensionality-reduction method for multiway data. Dimensionality reduction is often sought after since many high-dimensional tensors have low intrinsic rank relative to the dimension of the ambient measurement space.
N. Benjamin Erichson +3 more
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Tensor-CUR Decompositions for Tensor-Based Data [PDF]
SIAM Journal on Matrix Analysis and Applications, 2006 Motivated by numerous applications in which the data may be modeled by a variable subscripted by three or more indices, we develop a tensor-based extension of the matrix CUR decomposition. The tensor-CUR decomposition is most relevant as a data analysis tool when the data consist of one mode that is qualitatively different from the others. In this case,
Michael W. Mahoney +2 more
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Tensor Decompositions in Deep Learning [PDF]
CoRR, 2020 The paper surveys the topic of tensor decompositions in modern machine learning applications. It focuses on three active research topics of significant relevance for the community. After a brief review of consolidated works on multi-way data analysis, we consider the use of tensor decompositions in compressing the parameter space of deep learning ...
Bacciu D., Mandic D. P.
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DAO-CP: Data-Adaptive Online CP decomposition for tensor stream
PLoS ONE, 2022 How can we accurately and efficiently decompose a tensor stream? Tensor decomposition is a crucial task in a wide range of applications and plays a significant role in latent feature extraction and estimation of unobserved entries of data. The problem of
Sangjun Son +3 more
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Skew-symmetric tensor decomposition [PDF]
Communications in Contemporary Mathematics, 2019 We introduce the “skew apolarity lemma” and we use it to give algorithms for the skew-symmetric rank and the decompositions of tensors in [Formula: see text] with [Formula: see text] and [Formula: see text]. New algorithms to compute the rank and a minimal decomposition of a tritensor are also presented.
Enrique Esteban Arrondo +3 more
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On the Decomposition of Tensors by Contraction [PDF]
Reviews of Modern Physics, 1949 The decomposition of tensors into irreducible representations of the orthogonal groups is calculated for three and four dimensions. The connection is shown with the problem of the allowed values of ordinary and isotopic spin for a given symmetry of the spacial eigenfunction of a nuclear system.
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CoRR, 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.
Qibin Zhao +4 more
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Spectral Tensor-Train Decomposition [PDF]
SIAM Journal on Scientific Computing, 2016 The accurate approximation of high-dimensional functions is an essential task in uncertainty quantification and many other fields. We propose a new function approximation scheme based on a spectral extension of the tensor-train (TT) decomposition. We first define a functional version of the TT decomposition and analyze its properties. We obtain results
Daniele Bigoni +2 more
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IEEE Access, 2018 Reliable detection and recovery of a microseismic event in large volume of passive monitoring data is usually a challenging task due to the low signal-to-noise ratio environment.
Naveed Iqbal +5 more
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