题目：Optimal Reverse Prediction: A Unified Perspective on Supervised,
Unsupervised and Semi-supervised Learning 报告人：Linli Xu
Ph.D.
Department of Computing Science
University of Alberta, Canada 时间：9月29日（星期二） 10：00 - 11：00 地点：蒙民伟楼404会议室 摘要：
Training principles for unsupervised learning are often derived from
motivations that appear to be independent of supervised learning, causing a
proliferation of semisupervised training methods. In this talk we present a
simple unification of several supervised and unsupervised training principles
through the concept of optimal reverse prediction: predict the inputs from the
target labels, optimizing both over model parameters and any missing labels.
In particular, we show how supervised least squares, principal components
analysis, k-means clustering and normalized graph-cut clustering can all be
expressed as instances of the same training principle, differing only in
constraints made on the target labels. Natural forms of semi-supervised
regression and classification are then automatically derived, yielding semi-
supervised learning algorithms for regression and classification that,
surprisingly, are novel and refine the state of the art. These algorithms can
all be combined with standard regularizers and made non-linear via kernels. 简历：
Linli Xu is currently a postdoctoral fellow in Department of Computer Science
at University of Alberta, Canada. From 1997 to 2002, she studied for the
Bachelor degree at University of Science and Technology of China. She received
her Ph.D degree in Computer Science from University of Waterloo in 2007. Her
research area is Machine Learning, and her interests include unsupervised
learning and semi-supervised learning, clustering, large margin approaches,
support vector machines, optimization and convex programming.
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