Legendre decomposition for tensors*

DOI10.1088/1742-5468/ab3196zbMath1459.65055arXiv1802.04502OpenAlexW2949279215WikidataQ114096209 ScholiaQ114096209MaRDI QIDQ5854120

Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda

Publication date: 16 March 2021

Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1802.04502

Mathematics Subject Classification ID

Multilinear algebra, tensor calculus (15A69) Numerical methods for low-rank matrix approximation; matrix compression (65F55) Information geometry (statistical aspects) (62B11)

Related Items (1)

Non-negative low-rank approximations for multi-dimensional arrays on statistical manifold

Uses Software

• TensorToolbox
• MNIST
• TensorLy

Cites Work

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• Tensor Decompositions and Applications
• Information geometry and its applications
• An iterative row-action method for interval convex programming
• An Efficient Learning Procedure for Deep Boltzmann Machines
• Training Products of Experts by Minimizing Contrastive Divergence
• Efficient MATLAB Computations with Sparse and Factored Tensors
• Information geometry on hierarchy of probability distributions
• Information-Geometric Measure for Neural Spikes
• Continuous Lattices and Domains
• On Tensors, Sparsity, and Nonnegative Factorizations
• Learning the parts of objects by non-negative matrix factorization

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