CAREER: Learning Local Feature Relevance for Pattern Classification and Clustering

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Pattern classification is a broad research area with numerous applications ranging from science, engineering, target marketing, medical diagnosis and electronic commerce to weather forecasting. While pattern classification has shown promise in many areas of practical significance, it faces difficult challenges posed by real world problems, of which the most pronounced is the so-called curse of dimensionality: that is, the fact that the sample size required to perform accurate prediction on problems with high dimensionality is beyond feasibility. The goal of this project is to develop novel classification and clustering techniques to mitigate the curse of dimensionality and to reduce bias by estimating feature relevance and selecting features accordingly. In particular, this project has the following specific, measurable objectives: (1) to develop non-linear and flexible metrics for distance-based classifiers via kernel methods; (2) to construct effective ensembles by exploiting local feature relevance to perform adaptive sampling in feature space, and (3) to develop adaptive metrics for subspace clustering by measuring local correlation of data with respect to different dimensions.Given that so many problems of practical interest are high dimensional, this research will have significant impact in fields and applications as diverse as bioinformatics, security and intrusion detection, information and image retrieval. The PI's collaborative effort with biologists for the analysis of microarray data has the potential to contribute new data mining techniques to the HIV genomic knowledge domain, which in turn could eventually contribute to the customized diagnosis and treatment of AIDS.This project involves the training of both undergraduate and graduate students and includes a joint seminar course with the Department of Molecular Biology at George Mason University. The PI aims to facilitate the involvement of underrepresented groups in the science and engineering fields through her active participation in training activities and workshops in cooperation with the Multicultural Research Center at George Mason University.