| Autor: |
Donghoon Shin, Hyung Soon Kim |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
EURASIP Journal on Advances in Signal Processing, Vol 2024, Iss 1, Pp 1-14 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1687-6180 |
| DOI: |
10.1186/s13634-023-01092-1 |
| Popis: |
Abstract Mutual information (MI) quantifies the statistical dependency between a pair of random variables and plays a central role in signal processing and data analysis. Recent advances in machine learning have enabled the estimation of MI from a dataset using the expressive power of neural networks. In this study, we conducted a comparative experimental analysis of several existing neural estimators of MI between random vectors that model power spectrum features. We explored alternative models of power spectrum features by leveraging information-theoretic data processing inequality and bijective transformations. Empirical results demonstrated that each neural estimator of MI covered in this study has its limitations. In practical applications, we recommend the collective use of existing neural estimators in a complementary manner for the problem of estimating MI between power spectrum features. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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