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Conference Paper

Large Margin Non-Linear Embedding

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84331

Zien,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83845

Candela,  JQ
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Zien, A., & Candela, J. (2005). Large Margin Non-Linear Embedding. Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), 1065-1072.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D4B9-A
Abstract
It is common in classification methods to first place data in a vector space and then learn decision boundaries. We propose reversing that process: for fixed decision boundaries, we ``learnamp;amp;lsquo;amp;amp;lsquo; the location of the data. This way we (i) do not need a metric (or even stronger structure) -- pairwise dissimilarities suffice; and additionally (ii) produce low-dimensional embeddings that can be analyzed visually. We achieve this by combining an entropy-based embedding method with an entropy-based version of semi-supervised logistic regression. We present results for clustering and semi-supervised classification.