Supervised Dimensionality Reduction via Convex Optimization

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CIS Colloquium, Feb 18, 2009, 12:00PM – 01:00PM, Wachman 447

Supervised Dimensionality Reduction via Convex Optimization

Dr. Yuhong Guo

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 work, we propose 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 prevailing Gaussian assumptions of standard PCA, and produces a kernelized formulation for nonlinear supervised dimensionality reduction.

Yuhong Guo received her PhD degree in Computing Science from University of Alberta in 2007. Before that, she obtained her Master and Bachelor degrees in Computer Science from NanKai University, China. Before joining the Computer & Information Sciences Department at Temple University in 2009, she worked in Australian National University as a Research Fellow.