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Seminar Details


Date 7-1-2008
Time 15:00
Room/Location Sala conferenze-DISI-3 piano
Title Some novel developments in dimensionality reduction for classification
Speaker Guido Sanguinetti
Affiliation Department of Computer Science, University of Sheffield UK
Link https://www.disi.unige.it/index.php?eventsandseminars/seminars
Abstract Common dimensionality reduction techniques such as PCA and generalisations address the problem of finding lower dimensional representation of data based on variance considerations. However, the most varying directions need not be the most interesting: for example, if a high dimensional data set is known to contain clusters, the best dimensionality reduction will extract features that best discriminate between clusters, rather than capturing the most variance. We exploit this idea and introduce a latent variable model that extracts at maximum likelihood optimal discriminative features (in the sense of Fisher's discriminant) without access to label information. We then extend the framework to address the semi-supervised problem and possible non-linear extensions.
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