ACMS Applied Math Seminar : Samantha Sherman, University of Notre Dame


Location: 154 Hurley

Samantha Sherman
University of Notre Dame

3:30 PM
154 Hurley Hall

Tensor Decomposition and Data Analysis

After a brief intro to tensors and CP decompositions, I will talk about the problem of decomposing higher-order moment tensors, i.e., the sum of symmetric outerproducts of data vectors. Such a decomposition can be used to estimate the means in a Gaussian mixture model and for other applications in machine learning. The dth-order empirical moment tensor of a set of p observations of n variables is a symmetric d-way tensor. Our goal is to compute a low-rank tensor approximation. The challenge is that computing a low-rank approximation becomes prohibitively expensive quickly as the size of the problem grows because forming the initial moment tensor costs O(pn^d). Our contribution is avoiding formation of the moment tensor and computing the low-rank approximation implicitly. This reduces the number of operations and allows for efficient computation of higher-order moments. I will show examples using symmetric tensor decompositions to estimate symmetric tensor rank and recover means of Gaussian mixture models.

View Abstract

Full List of Applied Math Seminar Speakers





Originally published at

Add to Google Calendar
Download Event