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Mathematical Sciences Colloquium by Snigdhansu Chatterjee
Friday, Mar 30
1 p.m. - 2 p.m. Location: FN 2.102

Snigdhansu Chatterjee

School of Statistics

University of Minnesota

Geometric Statistics for High-Dimensional Data Analysis

We present a scheme of studying the geometry of high-dimensional data to discover patterns in it, using minimal parametric distributional assumptions. Our approach is to define multivariate quantiles and extremes, and develop a method of center-outward partial ordering of observations. We formulate methods for quantifying relationships among observed variables, thus generalizing the notions of regression and principal components. We propose tests for linear relations between variables in many dimensions using the geometric properties of the data, thus paving a way for checking whether recent developments involving Gaussian assumption or  sparsity of relations  are applicable. We devise geometric algorithms for detection of outliers in high dimensions, classification and supervised learning. Examples on the use the proposed methods will be provided. This is joint work with several  students.

 

Coffee to be served in FN 2.102 30 minutes prior to the talk.

Contact Info:
John Zweck, 972-883-6699
Questions? Email me.

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