Multi-view Metric Learning in Vector-valued Kernel Spaces
This addresses the problem of improving metric learning for multi-view data, which is common in applications like computer vision or bioinformatics, but it appears incremental as it builds on existing kernel and metric learning techniques.
The paper tackles metric learning for multi-view data by developing a method to learn within-view and between-view metrics in vector-valued kernel spaces, capturing multi-modal structure; it introduces a convex optimization formulation, an iterative algorithm, and a scalable approximation, showing competitive performance on real-world datasets against state-of-the-art methods.
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{ö}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.