A trace ratio maximization approach to multiple kernel-based dimensionality reduction

DOI10.1016/j.neunet.2013.09.004zbMath1296.68132OpenAlexW2029147317WikidataQ50710608 ScholiaQ50710608MaRDI QIDQ461199

Wenhao Jiang, Fu-lai Chung

Publication date: 10 October 2014

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2013.09.004

zbMATH Keywords

supervised learningunsupervised learningdimensionality reductiongraph embeddingkernel learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

An adaptive deep Q-learning strategy for handwritten digit recognition ⋮ Multiple kernel dimensionality reduction via ratio-trace and marginal Fisher analysis

Uses Software

• LIBSVM

Cites Work

• Unnamed Item
• Unnamed Item
• Principal component analysis.
• The Trace Ratio Optimization Problem
• Introduction to Information Retrieval
• Canonical Correlation Analysis: An Overview with Application to Learning Methods

This page was built for publication: A trace ratio maximization approach to multiple kernel-based dimensionality reduction