Multi-view Metric Learning in Vector-valued Kernel Spaces

We consider the problem of metric learning for multi-view data and present a novel method for learning within-view as well as between-view metrics in vector-valued kernel spaces, as a way to capture multi-modal structure of the data. We formulate two convex optimization problems to jointly learn the...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Huusari, Riikka, Hachem Kadri, Capponi, Cécile
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 21.03.2018
Subjects
Algorithms
Computational geometry
Convexity
Iterative methods
Kernels
Machine learning
Optimization
Online AccessGet full text

Cover

More Information
Summary:We consider the problem of metric learning for multi-view data and present a novel method for learning within-view as well as between-view metrics in vector-valued kernel spaces, as a way to capture multi-modal structure of the data. We formulate two convex optimization problems to jointly learn the metric and the classifier or regressor in kernel feature spaces. An iterative three-step multi-view metric learning algorithm is derived from the optimization problems. In order to scale the computation to large training sets, a block-wise Nystr{\"o}m approximation of the multi-view kernel matrix is introduced. We justify our approach theoretically and experimentally, and show its performance on real-world datasets against relevant state-of-the-art methods.
ISSN:2331-8422