Roshan Joseph : Support Points: An Optimal and Model-Free Method for Subsampling Big Data

Posted by Ian Tobasco , part of the Departmental Colloquium.

At

Nov. 22, 2019, 1:30 p.m.

In

636 SEO

Abstract

This talk presents a novel method called support points, which can be used for optimal and model-free subsampling of big data. This method has important applications to many practical problems in statistics and machine learning, particularly when the available data is plentiful and high-dimensional, but the processing of such data is expensive due to computation or storage costs. We also propose an extension of the method called Projected Support Points to deal with high dimensional data, which ensures that the data is well-reduced on low-dimensional projections of the data space.

Note the unusual time.