PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Large Margin Non-Linear Embedding
Alexander Zien and Joaquin Quinonero Candela
In: ICML 2005, 7-11 Aug 2005, Bonn, Germany.


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 ``learn'' 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.

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EPrint Type:Conference or Workshop Item (Paper)
Additional Information:Software available at
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1015
Deposited By:Alexander Zien
Deposited On:13 July 2005