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Activity Number: 178 - Novel Applications and Extensions of Dimension Reduction Methods
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #305282 Presentation
Title: Graph-Based Dependency Criterion with Applications in Biology
Author(s): Salimeh Yasaei Sekeh* and Alfred O. Hero
Companies: University of Michigan and University of Michigan
Keywords: Graph-based dependency; Multi-labeled variables ; Computational complexity; Feature selection ; Biology application ; Information theoretic measures

Several algorithms to learn the dependency between a pair of multivariate random variables directly from a data sample have been proposed in the past. This work proposes a new graph-based dependency criterion inspired by geometry of graphs and information theoretic measures to estimate dependency between multi-labels variables. The advantages of our proposed dependency estimator are demonstrated in a series of simulations. This approach results in an efficient and fast non-parametric implementation of dependency estimation with broad applications in biology. For instance, the proposed technique is applied to the genetic data set to filter out redundant features.

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

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