Conference proceeding
Learning kernel combination from noisy pairwise constraints
2012 IEEE Statistical Signal Processing Workshop (SSP), pp.752-755
08/2012
DOI: 10.1109/SSP.2012.6319813
Abstract
We consider the problem of learning the combination of multiple kernels given noisy pairwise constraints, which is in contrast to most of the existing studies that assume perfect pairwise constraints. This problem is particularly important when the pairwise constraints are derived from side information such as hyperlinks and paper citations. We propose a probabilistic approach for learning the combination of multiple kernels and show that under appropriate assumptions, the combination weights learned by the proposed approach from the noisy pairwise constraints converge to the optimal weights learned from perfectly labeled pairwise constraints. Empirical studies on data clustering using the learned combined kernel verify the effectiveness of the proposed approach.
Details
- Title: Subtitle
- Learning kernel combination from noisy pairwise constraints
- Creators
- Tianbao Yang - Michigan State UniversityRong Jin - Michigan State UniversityA. K Jain - Michigan State University
- Resource Type
- Conference proceeding
- Publication Details
- 2012 IEEE Statistical Signal Processing Workshop (SSP), pp.752-755
- Publisher
- IEEE
- DOI
- 10.1109/SSP.2012.6319813
- ISSN
- 2373-0803
- Language
- English
- Date published
- 08/2012
- Academic Unit
- Computer Science
- Record Identifier
- 9984259437802771
Metrics
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