Learning Kernels from Indefinite Similarities
- Creators
- Chen, Yihua
- Gupta, Maya R.
- Recht, Benjamin
Abstract
Similarity measures in many real applications generate indefinite similarity matrices. In this paper, we consider the problem of classification based on such indefinite similarities. These indefinite kernels can be problematic for standard kernel-based algorithms as the optimization problems become nonconvex and the underlying theory is invalidated. In order to adapt kernel methods for similarity-based learning, we introduce a method that aims to simultaneously find a reproducing kernel Hilbert space based on the given similarities and train a classifier with good generalization in that space. The method is formulated as a convex optimization problem. We propose a simplified version that can reduce overfitting and whose associated convex conic program can be solved effiently. We compare the proposed simplified version with six other methods on a collection of real data sets.
Additional Information
Copyright 2009 by the author(s)/owner(s). This work was funded by the Office of Naval Research.Attached Files
Published - p145-chen.pdf
Files
Name | Size | Download all |
---|---|---|
md5:5dcdd72b451c74f3fd3247bed9706646
|
906.8 kB | Preview Download |
Additional details
- Eprint ID
- 69864
- Resolver ID
- CaltechAUTHORS:20160823-154321422
- Office of Naval Research (ONR)
- Created
-
2016-08-23Created from EPrint's datestamp field
- Updated
-
2021-11-11Created from EPrint's last_modified field