Relatively-paired space analysis: learning a latent common space from relatively-paired observations

DOI10.1007/S11263-014-0783-8zbMath1398.68585OpenAlexW1999210118MaRDI QIDQ1799958

Kwan-Yee K. Wong, Zhanghui Kuang

Publication date: 19 October 2018

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11263-014-0783-8

zbMATH Keywords

structural learningcutting-plane algorithmabsolutely paired observationsmulti-modality analysismulti-view analysisrelatively paired observations

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Machine vision and scene understanding (68T45)

Related Items (1)

Relatively-paired space analysis: learning a latent common space from relatively-paired observations

Uses Software

• L-BFGS
• Pegasos
• SIFT
• PubFig

Cites Work

• Pegasos
• A novel ensemble construction method for multi-view data using random cross-view correlation between within-class examples
• Pegasos: primal estimated sub-gradient solver for SVM
• On the limited memory BFGS method for large scale optimization
• Relatively-paired space analysis: learning a latent common space from relatively-paired observations
• Cutting-plane training of structural SVMs
• Large-Scale Machine Learning with Stochastic Gradient Descent
• On the Early History of the Singular Value Decomposition
• 10.1162/jmlr.2003.3.4-5.993
• Canonical Correlation Analysis: An Overview with Application to Learning Methods

This page was built for publication: Relatively-paired space analysis: learning a latent common space from relatively-paired observations