Allerton 2015 Paper Abstract

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Paper ThD6.1

Makur, Anuran (Massachusetts Institute of Technology), Zheng, Lizhong (Massachusetts Institute of Technology)

Bounds between Contraction Coefficients

Scheduled for presentation during the Regular Session "Information Theory IV" (ThD6), Thursday, October 1, 2015, 15:30−15:50, Vistior Center

53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA

This information is tentative and subject to change. Compiled on November 19, 2019

Keywords Information Theory, Statistical Signal Processing

Abstract

In this paper, we delineate how the contraction coefficient of the strong data processing inequality for KL divergence can be used to learn likelihood models. We then present an alternative formulation that forces the input KL divergence to vanish, and achieves a contraction coefficient equivalent to the squared maximal correlation using a linear algebraic solution. To analyze the performance loss in using this simple but suboptimal procedure, we bound these coefficients in the discrete and finite regime, and prove their equivalence in the Gaussian regime.

 

 

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