Abstract
We give an asymptotic expansion of the relative entropy between the heat kernel q Z(t,z,w) of a compact Riemannian manifold Z and the normalized Riemannian volume for small values of t and for a fixed element z∈Z. We prove that coefficients in the expansion can be expressed as universal polynomials in the components of the curvature tensor and its covariant derivatives at z, when they are expressed in terms of normal coordinates. We describe a method to compute the coefficients, and we use the method to compute the first three coefficients. The asymptotic expansion is necessary for an unsupervised machine-learning algorithm called the Diffusion Variational Autoencoder.
| Original language | English |
|---|---|
| Article number | 101642 |
| Number of pages | 23 |
| Journal | Applied and Computational Harmonic Analysis |
| Volume | 71 |
| DOIs | |
| Publication status | Published - Jul 2024 |
Keywords
- Entropy
- Heat kernel
- Kullback-Leibler divergence
- Parametrix expansion
- Variational autoencoder
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