Intel Research Seminar

DATE: August 7, 2003
TIME: Noon - 1:30 pm
PLACE: Intel Seminar (417 S. Craig Street - 3rd Floor)

Nuno Vasconcelos

Information Theoretic Feature Selection for Large-scale Classification Problems

Feature selection remains a challenging problem for large-scale classification problems, involving large numbers of classes and significant amounts of training data per class, such as visual recognition, speech recognition, or information retrieval. In this work, we introduce some new connections between information theoretic (infomax) feature selection methods and the Bayes classification error to develop a new family of feature selection algorithms. The concept of marginal diversity is introduced, leading to a discriminant feature selection principle of extreme computational simplicity. The relationships between infomax and maximization of marginal diversity are studied, uncovering a family of classification problems for which infomax-optimal feature selection does not require combinatorial search. An analysis of this family in light of recent studies on the statistics of natural images suggests a generalization of the principle of maximum marginal diversity that allows explicit control of the trade-off between complexity and infomax-optimality. Experimental results, in the context of visual recognition, indicate that the optimal trade-off occurs at low-levels of complexity. The corresponding algorithm is shown to significantly outperform existing scalable feature selection techniques.

Nuno Vasconcelos received a “Licenciatura” from the University of Porto, Portugal, a SM and a PhD from MIT. He was a member of the research staff at the Compaq Cambridge Research Laboratory, and then HP Cambridge Research Laboratory, between 2000 and 2003. In March 2003, he joined the Department of Electrical and Computer Engineering at the University of California, San Diego, where he is an assistant professor. His interests are in machine vision, machine learning, statistical signal processing, and multimedia.

For Further Seminar Info:
Contact Kim Kaan, 412-605-1203, or visit

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