Tensor SOM and tensor GTM: nonlinear tensor analysis by topographic mappings

DOI10.1016/j.neunet.2016.01.013zbMath1414.68061DBLPjournals/nn/IwasakiF16OpenAlexW2286169297WikidataQ39908981 ScholiaQ39908981MaRDI QIDQ2418142

Tetsuo Furukawa, Tohru Iwasaki

Publication date: 3 June 2019

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2016.01.013

zbMATH Keywords

self-organizing maptensor decompositionrelational datagenerative topographic map

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Data structures (68P05)

Uses Software

• GTM

Cites Work

• Tensor Decompositions and Applications
• A kernel-based framework to tensorial data analysis
• A survey of multilinear subspace learning for tensor data
• Email surveillance using non-negative matrix factorization
• Variational Bayesian generative topographic mapping
• Self-organized formation of topologically correct feature maps
• Topographic organization of nerve fields
• Self-organizing maps: Generalizations and new optimization techniques
• Developments of the generative topographic mapping
• Self-organizing neural projections
• Positive tensor factorization
• A Survey and Empirical Comparison of Object Ranking Methods
• A Multilinear Singular Value Decomposition
• Self-organizing maps.

This page was built for publication: Tensor SOM and tensor GTM: nonlinear tensor analysis by topographic mappings