Results 111 to 120 of about 1,665,186 (233)

Tensor Decompositions in Deep Learning

, 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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Nonnegative Tensor Factorization, Completely Positive Tensors and an Hierarchical Elimination Algorithm [PDF]

, 2013
Nonnegative tensor factorization has applications in statistics, computer vision, exploratory multiway data analysis and blind source separation. A symmetric nonnegative tensor, which has a symmetric nonnegative factorization, is called a completely ...
Qi, Liqun, Xu, Changqing, Xu, Yi
core  

Personalized Web Service Recommendation Based on QoS Prediction and Hierarchical Tensor Decomposition

IEEE Access, 2019
Web service recommendation based on the quality of service (QoS) is important for users to find the exact Web service among many functionally similar Web services.
Tian Cheng, Junhao Wen, Qingyu Xiong, Jun Zeng, Wei Zhou, Xueyuan Cai   +5 more
doaj   +1 more source

Hankel Tensors: Associated Hankel Matrices and Vandermonde Decomposition

, 2014
Hankel tensors arise from applications such as signal processing. In this paper, we make an initial study on Hankel tensors. For each Hankel tensor, we associate it with a Hankel matrix and a higher order two-dimensional symmetric tensor, which we call ...
Qi, Liqun
core  

Tensor decompositions

, 2020
Tensors are combinations of several vectors such that a bigger vector space, also calledthe tensor space, emerges. The tensor space has a richer structure that that of theseparate vector spaces. Tensors are universally used in quantum mechanics to modelmutipartite systems. Tensor decompositions aim to write these tensors as combina-tions of elements of
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Clustering Patients with Tensor Decomposition

, 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. We present the reasons why this approach is preferable for tasks such as clustering patient records, to ...
Ruffini, Matteo, Gavaldà Mestre, Ricard, Limón, Esther   +2 more
openaire   +3 more sources

Scalable Symmetric Tucker Tensor Decomposition

SIAM Journal on Matrix Analysis and Applications
We study the best low-rank Tucker decomposition of symmetric tensors. The motivating application is decomposing higher-order multivariate moments. Moment tensors have special structure and are important to various data science problems. We advocate for projected gradient descent (PGD) method and higher-order eigenvalue decomposition (HOEVD ...
Ruhui Jin, Joe Kileel, Tamara G. Kolda, Rachel Ward   +3 more
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Underdetermined blind source separation based on third‐order cumulant and tensor compression

IET Radar, Sonar & Navigation
A method for Underdetermined Blind Source Separation is proposed using third‐order cumulants and tensor compression. To effectively suppress symmetrical distributed noise, the third‐order cumulant is considered.
Weilin Luo, Xiaobai Li, Hongbin Jin, Hao Li, Kai Yuan, Ruijuan Yang   +5 more
doaj   +1 more source

Robust Low-rank Tensor Decomposition with the L2 Criterion. [PDF]

Technometrics, 2023
Heng Q, Chi EC, Liu Y.
europepmc   +1 more source

Tensor decompositions in rank +1

, 2020
We prove (without exceptions) the existence of irredundant tensor decompositions with the number of addenda equal to rank $+1$. We also discuss the existence of decompositions with more than the tensor rank terms, which are concise, while the original tensor is not concise.
openaire   +4 more sources