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Activity Number: 119 - Statistical Learning Applications for Autonomous Systems in Defense and National Security
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Statistics in Defense and National Security
Abstract #310928
Title: Tallis: A Statistical Approach for Dimension Reduction of Mixed-Type Variables
Author(s): Alexander Foss*
Companies: Sandia National Laboratories
Keywords: Dimension reduction; mixed-type data; anomaly detection

A common challenge in a broad array of disciplines is dimension reduction of variables of mixed-type. In this talk, a novel statistical model for dimension reduction of combinations of continuous, count, and categorical data is introduced and contrasted with existing approaches in the literature and practice. An Expectation-Maximization (EM) algorithm for estimating model parameters is derived, and an algorithm for parallel EM chains is discussed. The performance of the method is illustrated using examples drawn from anomaly detection and graph analysis applications.

SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525

Authors who are presenting talks have a * after their name.

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