Turbo‐SMT: Parallel coupled sparse matrix‐Tensor factorizations and applications

DOI10.1002/sam.11315OpenAlexW2468256651WikidataQ39347894 ScholiaQ39347894MaRDI QIDQ4970200

No author found.

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://pureadmin.qub.ac.uk/ws/files/124764278/paper.pdf

zbMATH Keywords

tensoralgorithmparallelsparsespeedupfMRI datacoupled matrix-tensor factorizationneurosemantics

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items (2)

Bayesian multi-tensor factorization ⋮ Turbo-SMT

Uses Software

• TensorToolbox
• GigaTensor

Cites Work

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• Tensor Decompositions and Applications
• A randomized algorithm for a tensor-based generalization of the singular value decomposition
• A comparison of algorithms for fitting the PARAFAC model
• Simultaneous analysis of coupled data blocks differing in size: a comparison of two weighting schemes
• CANDELINC: A general approach to multidimensional analysis of many-way arrays with linear constraints on parameters
• The Moore-Penrose inverse of a partitioned matrix \(M=\begin{pmatrix} D&D\\ B&C\end{pmatrix}\)
• Efficient MATLAB Computations with Sparse and Factored Tensors
• A Multilinear Singular Value Decomposition
• Tensor-CUR Decompositions for Tensor-Based Data
• Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition

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