Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Bounds on mutual information of mixture data for classification tasks

Not Accessible

Your library or personal account may give you access

Abstract

To quantify the optimum performance for classification tasks, the Shannon mutual information is a natural information-theoretic metric, as it is directly related to the probability of error. The data produced by many imaging systems can be modeled by mixture distributions. The mutual information between mixture data and the class label does not have an analytical expression nor any efficient computational algorithms. We introduce a variational upper bound, a lower bound, and three approximations, all employing pair-wise divergences between mixture components. We compare the new bounds and approximations with Monte Carlo stochastic sampling and bounds derived from entropy bounds. To conclude, we evaluate the performance of the bounds and approximations through numerical simulations.

© 2022 Optica Publishing Group

Full Article  |  PDF Article

Data availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (10)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (33)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved