Results 11 to 20 of about 234,207 (337)
Smoothed Analysis of Tensor Decompositions [PDF]
Proceedings of the forty-sixth annual ACM symposium on Theory of computing, 2014 Low rank tensor decompositions are a powerful tool for learning generative models, and uniqueness results give them a significant advantage over matrix decomposition methods. However, tensors pose significant algorithmic challenges and tensors analogs of
Anandkumar A. +12 more
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Spectral tensor-train decomposition [PDF]
SIAM Journal on Scientific Computing, 2015 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.
Bigoni, Daniele +2 more
core +6 more sources
Clustering Patients with Tensor Decomposition [PDF]
CoRR, 2017 In this paper we present a method for the unsupervised clustering of high-dimensional binary data, with a special focus on electronic healthcare records. We present a robust and efficient heuristic to face this problem using tensor decomposition.
Gavaldà, Ricard +2 more
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Symmetric Tensor Decomposition
Linear Algebra and its Applications, 2009 Publication in the conference proceedings of EUSIPCO, Glasgow, Scotland ...
Jérôme Brachat +3 more
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Hermitian Tensor Decompositions [PDF]
SIAM Journal on Matrix Analysis and Applications, 2020 Hermitian tensors are generalizations of Hermitian matrices, but they have very different properties. Every complex Hermitian tensor is a sum of complex Hermitian rank-1 tensors. However, this is not true for the real case. We study basic properties for Hermitian tensors such as Hermitian decompositions and Hermitian ranks. For canonical basis tensors,
Jiawang Nie, Zi Yang
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Counting Tensor Rank Decompositions [PDF]
Universe, 2021 Tensor rank decomposition is a useful tool for geometric interpretation of the tensors in the canonical tensor model (CTM) of quantum gravity. In order to understand the stability of this interpretation, it is important to be able to estimate how many tensor rank decompositions can approximate a given tensor.
Dennis Obster, Naoki Sasakura
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On the optimization landscape of tensor decompositions [PDF]
Mathematical Programming, 2020 Best paper in the NIPS 2016 Workshop on Nonconvex Optimization for Machine Learning: Theory and Practice.
Rong Ge 0001, Tengyu Ma 0001
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Multi-Modal Image Fusion Based on Matrix Product State of Tensor
Frontiers in Neurorobotics, 2021 Multi-modal image fusion integrates different images of the same scene collected by different sensors into one image, making the fused image recognizable by the computer and perceived by human vision easily.
Yixiang Lu +4 more
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Orthogonal decomposition of tensor trains [PDF]
Linear and Multilinear Algebra, 2021 In this paper we study the problem of decomposing a given tensor into a tensor train such that the tensors at the vertices are orthogonally decomposable. When the tensor train has length two, and the orthogonally decomposable tensors at the two vertices are symmetric, we recover the decomposition by considering random linear combinations of slices ...
Karim Halaseh +2 more
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Design and Implementation of Tucker Decomposition Module Based on CUDA and CUBLAS [PDF]
Jisuanji gongcheng, 2019 Because tensor Tucker decomposition is widely used in image processing,face recognition,signal processing and other fields,Tucker decomposition algorithm becomes a key research object.However,the current popular Tucker decomposition algorithm needs to ...
ZHOU Qi,CHAI Xiaoli,MA Kejie,YU Zeren
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