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 | 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Hybrid, Singapore Duration: May 23 2022 → May 27 2022 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2022-May |
ISSN (Print) | 1520-6149 |
Conference
Conference | 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 |
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Country/Territory | Singapore |
City | Hybrid |
Period | 5/23/22 → 5/27/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE
Keywords
- Divergence
- Imbalanced classification
- Information theoretic learning
- Kernel methods
- Subsampling
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering