Applied Math Seminar

09/26/2019 - 11:00am to 12:00pm
POT 745
Speaker(s) / Presenter(s): 
Luis Sanchez Giraldo, University of Kentucky

Title: Information Theoretic Learning with Infinitely Divisible Kernels

Abstract: In this work, we introduce a framework for information theoretic learning based on an entropy-like functional defined on positive definite matrices. The proposed functional, which is based on Renyi's axiomatic definition of entropy, provides a quantity that can be estimated from data and applied as an objective function in different machine learning problems. As an application example, we derive a supervised metric learning algorithm using a matrix-based analogue to conditional entropy with results comparable with the state of the art.

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