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41

ICML
2005
IEEE

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Large margin non-linear embedding

14 years 11 months ago

Large margin non-linear embedding

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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 entropybased embedding method with an entropybased version of semi-supervised logistic regression. We present results for clustering and semi-supervised classification.

Alexander Zien, Joaquin Quiñonero Candela

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Decision Boundaries | ICML 2005 | Machine Learning | Produce Low-dimensional Embeddings | Semi-supervised Logistic Regression |

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Added	17 Nov 2009
Updated	17 Nov 2009
Type	Conference
Year	2005
Where	ICML
Authors	Alexander Zien, Joaquin Quiñonero Candela

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