Localized Multiple Kernel Learning---A Convex Approach
Machine Learning
2016-10-14 v2
Abstract
We propose a localized approach to multiple kernel learning that can be formulated as a convex optimization problem over a given cluster structure. For which we obtain generalization error guarantees and derive an optimization algorithm based on the Fenchel dual representation. Experiments on real-world datasets from the application domains of computational biology and computer vision show that convex localized multiple kernel learning can achieve higher prediction accuracies than its global and non-convex local counterparts.
Cite
@article{arxiv.1506.04364,
title = {Localized Multiple Kernel Learning---A Convex Approach},
author = {Yunwen Lei and Alexander Binder and Ürün Dogan and Marius Kloft},
journal= {arXiv preprint arXiv:1506.04364},
year = {2016}
}
Comments
to appear in ACML 2016