Nicolas Fraiman, “Clustering and classification based on disjoint features”
Nicolas Fraiman, PhD Assistant Professor
Statistics and Operations Research
University of North Carolina
at Chapel Hill
Abstract: Dimensionality reduction techniques are often needed when working with high-dimensional data. In this talk we introduce two related methods for clustering and classification with feature selection. The unsupervised case can be framed as special type of biclustering problem where we try to cluster the rows and columns of the data matrix in diagonal blocks. We provide a formulation and algorithm similar to k-means with a modified within-cluster sum of squares. In the supervised case, our method is similar to the nearest centroid classifier but uses disjoint sets of features for each class and is able to perform feature selection. We demonstrate and compare the performance of our methods on some gene expression datasets.
Biography: Nicolas Fraiman is an Assistant Professor in the Department of Statistics and Operations Research at the University of North Carolina at Chapel Hill. Prior to this, he held postdoctoral positions at University of Pennsylvania and Harvard University. He completed his Ph.D. in Mathematics at McGill University in 2013. His research interests include random structures, combinatorial statistics, and randomized algorithms.
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Meeting ID: 993 0411 4570