Loading [a11y]/accessibility-menu.js
Mutual information approximation via maximum likelihood estimation of density ratio | IEEE Conference Publication | IEEE Xplore
Scheduled Maintenance: On Tuesday, 25 February, IEEE Xplore will undergo scheduled maintenance from 1:00-5:00 PM ET (1800-2200 UTC). During this time, there may be intermittent impact on performance. We apologize for any inconvenience.

Mutual information approximation via maximum likelihood estimation of density ratio


Abstract:

We propose a new method of approximating mutual information based on maximum likelihood estimation of a density ratio function. The proposed method, Maximum Likelihood Mu...Show More

Abstract:

We propose a new method of approximating mutual information based on maximum likelihood estimation of a density ratio function. The proposed method, Maximum Likelihood Mutual Information (MLMI), possesses useful properties, e.g., it does not involve density estimation, the global optimal solution can be efficiently computed, it has suitable convergence properties, and model selection criteria are available. Numerical experiments show that MLMI compares favorably with existing methods.
Date of Conference: 28 June 2009 - 03 July 2009
Date Added to IEEE Xplore: 18 August 2009
ISBN Information:

ISSN Information:

Conference Location: Seoul, Korea (South)

References

References is not available for this document.