Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization

DOI10.1162/NECO_a_00614zbMath1415.68173arXiv1206.4614OpenAlexW2157911873WikidataQ44744384 ScholiaQ44744384MaRDI QIDQ5383793

Bo Dai, Gang Niu, Makoto Yamada, Masashi Sugiyama

Publication date: 20 June 2019

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

Full work available at URL: https://arxiv.org/abs/1206.4614

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Ordinal margin metric learning and its extension for cross-distribution image data, SPACETIME DISCOUNTED VALUE OF NETWORK CONNECTIVITY, Classification from Triplet Comparison Data, Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization, Cross-Domain Metric and Multiple Kernel Learning Based on Information Theory

Uses Software

• GSML

Cites Work

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• Kernel dimension reduction in regression
• Information Theory and Statistical Mechanics
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Metric Learning
• Maximum Entropy Distribution Estimation with Generalized Regularization
• Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization
• Model Selection and Estimation in Regression with Grouped Variables