Bounds between contraction coefficients
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Zheng_Bounds between.pdf
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522.89 KB
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Author(s)
Makur, Anuran
Zheng, Lizhong
Date Issued
April 2016
Journal
2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Makur, Anuran, and Lizhong Zheng. "Bounds between Contraction Coefficients." 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 29 September - 2 October, 2015, Monticello, IL, IEEE, 2015, pp. 1422–29.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ALLERTON.2015.7447175
