Skip navigation
Skip navigation

Supervised exponential family principal component analysis via convex optimization

Guo, Yuhong


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
Source: Advances in Neural Information Processing Systems 21


File Description SizeFormat Image
01_Guo_Supervised_exponential_family_2008.pdf251.19 kBAdobe PDF    Request a copy

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  20 July 2017/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator