Variational Weighting for Kernel Density Ratios
Kernel density estimation (KDE) is integral to a range of generative and discriminative tasks in machine learning. Drawing upon tools from the multidimensional calculus of variations, we derive an optimal weight function that reduces bias in standard kernel density estimates for density ratios, lead...
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| Veröffentlicht in: | arXiv.org 2023-11 |
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| creator | Yoon, Sangwoong Park, Frank C Yun, Gunsu S Kim, Iljung Yung-Kyun Noh |
| description | Kernel density estimation (KDE) is integral to a range of generative and discriminative tasks in machine learning. Drawing upon tools from the multidimensional calculus of variations, we derive an optimal weight function that reduces bias in standard kernel density estimates for density ratios, leading to improved estimates of prediction posteriors and information-theoretic measures. In the process, we shed light on some fundamental aspects of density estimation, particularly from the perspective of algorithms that employ KDEs as their main building blocks. |
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| subjects | Algorithms Calculus of variations Density Estimates Information theory Kernels Machine learning Weight reduction Weighting functions |
| title | Variational Weighting for Kernel Density Ratios |
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