Large margin non-linear embedding

10 years 6 months ago
Large margin non-linear embedding
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
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2005
Where ICML
Authors Alexander Zien, Joaquin Quiñonero Candela
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