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Supervised exponential family principal component analysis via convex optimization

Guo, Yuhong

Description

Recently, supervised dimensionality reduction has been gaining attention, owing to the realization that data labels are often available and indicate important underlying structure in the data. In this paper, we present a novel convex supervised dimensionality reduction approach based on exponential family PCA, which is able to avoid the local optima of typical EM learning. Moreover, by introducing a sample-based approximation to exponential family models, it overcomes the limitation of the...[Show more]

CollectionsANU Research Publications
Date published: 2008
Type: Conference paper
URI: http://hdl.handle.net/1885/51906
Source: Advances in Neural Information Processing Systems 21

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