Low-rank bilinear classification: efficient convex optimization and extensions

DOI10.1007/s11263-014-0709-5zbMath1328.68183OpenAlexW2020956195MaRDI QIDQ901815

Takumi Kobayashi

Publication date: 6 January 2016

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

Full work available at URL: https://doi.org/10.1007/s11263-014-0709-5

zbMATH Keywords

convex optimizationlow-rank matrixmultiple kernel learningbilinear classifiercross modal learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10) Machine vision and scene understanding (68T45)

Related Items (1)

Normal Cones Intersection Rule and Optimality Analysis for Low-Rank Matrix Optimization with Affine Manifolds

Uses Software

• LIBSVM
• LIBLINEAR
• SimpleMKL
• PASCAL VOC

Cites Work

• Foundations of bilevel programming
• Numerical Optimization
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Learning the parts of objects by non-negative matrix factorization
• Choosing multiple parameters for support vector machines
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Low-rank bilinear classification: efficient convex optimization and extensions