Abstract
We introduce a divergence measure between data distributions based on operators in reproducing kernel Hilbert spaces defined by kernels. The empirical estimator of the divergence is computed using the eigenvalues of positive definite Gram matrices that are obtained by evaluating the kernel over pairs of data points. The new measure shares similar properties to Jensen-Shannon divergence. Convergence of the proposed estimators follows from concentration results based on the difference between the ordered spectrum of the Gram matrices and the integral operators associated with the population quantities. The proposed measure of divergence avoids the estimation of the probability distribution underlying the data. Numerical experiments involving comparing distributions and applications to sampling unbalanced data for classification show that the proposed divergence can achieve state of the art results.
| Original language | English |
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| Title of host publication | 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings |
| Pages | 4313-4317 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781665405409 |
| DOIs | |
| State | Published - 2022 |
| Event | 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022 - Hybrid, Singapore Duration: May 22 2022 → May 27 2022 |
Publication series
| Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
|---|---|
| Volume | 2022-May |
| ISSN (Print) | 1520-6149 |
Conference
| Conference | 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022 |
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| Country/Territory | Singapore |
| City | Hybrid |
| Period | 5/22/22 → 5/27/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE
Funding
This material is based upon work supported by the Office of the Under Secretary of Defense for Research and Engineering under award number FA9550-21-1-0227. Austin Brockmeier’s effort were sponsored by the Department of the Navy, Office of Naval Research under ONR award number N00014-21-1-2300.
| Funders | Funder number |
|---|---|
| Office of the Under Secretary of Defense for Research and Engineering | FA9550-21-1-0227 |
| Office of Naval Research Naval Academy | N00014-21-1-2300 |
| Office of Naval Research Naval Academy | |
| U.S. Navy Air Systems Command |
Keywords
- Divergence
- Imbalanced classification
- Information theoretic learning
- Kernel methods
- Subsampling
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering