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  Deterministic annealing for semi-supervised kernel machines

Sindhwani, V., Keerthi, S., & Chapelle, O. (2006). Deterministic annealing for semi-supervised kernel machines. In W. Cohen, & A. Moore (Eds.), ICML '06: Proceedings of the 23rd International Conference on Machine Learning (pp. 841-848). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D135-A Version Permalink: https://hdl.handle.net/21.11116/0000-0004-9A83-A
Genre: Conference Paper

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https://dl.acm.org/citation.cfm?doid=1143844.1143950 (Publisher version)
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 Creators:
Sindhwani, V, Author
Keerthi, SS, Author
Chapelle, O1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: An intuitive approach to utilizing unlabeled data in kernel-based
classification algorithms is to simply treat the unknown labels as
additional optimization variables. For margin-based loss functions,
one can view this approach as attempting to learn low-density
separators. However, this is a hard optimization problem to solve in
typical semi-supervised settings where unlabeled data is abundant.
The popular Transductive SVM algorithm is a
label-switching-retraining procedure that is known to be susceptible
to local minima. In this paper, we present a global optimization
framework for semi-supervised Kernel machines where an easier
problem is parametrically deformed to the original hard problem and
minimizers are smoothly tracked. Our approach is motivated from
deterministic annealing techniques and involves a sequence of convex
optimization problems that are exactly and efficiently solved. We
present empirical results on several synthetic and real world
datasets that demonstrate the effectiveness of our approach.

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 Dates: 2006-06
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1145/1143844.1143950
BibTex Citekey: 4061
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Title: 23rd International Conference on Machine Learning (ICML 2006)
Place of Event: Pittsburgh, PA, USA
Start-/End Date: 2006-06-25 - 2006-06-29

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Title: ICML '06: Proceedings of the 23rd International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Cohen, W, Editor
Moore, A, Editor
Affiliations:
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Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 841 - 848 Identifier: ISBN: 1-59593-383-2