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A Survey on Tensor Techniques and Applications in Machine Learning
IEEE Access, 2019 This survey gives a comprehensive overview of tensor techniques and applications in machine learning. Tensor represents higher order statistics. Nowadays, many applications based on machine learning algorithms require a large amount of structured high ...
Yuwang Ji, Qiang Wang, Xuan Li, Jie Liu
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上海师范大学学报. 自然科学版, 2021 By using the sparse scattering characteristics of the millimeter wave channel and the spatial structure of the tensor, a channel estimation method based on random grid tensor decomposition was proposed.
ZHANG Jing +3 more
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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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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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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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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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Spline- and tensor-based signal reconstruction : from structure analysis to high-performance algorithms to multiplatform implementations and medical applications [PDF]
, 2015 The problem of signal reconstruction is of fundamental practical value for many applications associated with the field of signal and image processing.
Morozov, Oleksii
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, 2022 Early identification of coronary artery disease (CAD) can facilitate timely clinical intervention and save lives. This study aims to develop a machine learning framework that uses tensor analysis on heart rate (HR) signals to automate the CAD detection ...
Acharya, U. Rajendra +3 more
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Finsler geometry on higher order tensor fields and applications to high angular resolution diffusion imaging. [PDF]
, 2009 We study 3D-multidirectional images, using Finsler geometry. The application considered here is in medical image analysis, specifically in High Angular Resolution Diffusion Imaging (HARDI) (Tuch et al. in Magn. Reson. Med.
Florack, L.M.J. +9 more
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