A Note on the Kullback-Leibler Divergence for the von Mises-Fisher distribution
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Abstract (arXiv)
We present a derivation of the Kullback Leibler (KL)-Divergence (also known as Relative Entropy) for the von Mises Fisher (VMF) Distribution in d-dimensions.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2015.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 1; 2026: 1.
5 papers.
- 2022Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-tailed Learningcited by 17
- 2024Dynamic Feature Learning and Matching for Class-Incremental Learningcited by 1
- 2024Probabilistic U-Net with Kendall Shape Spaces for Geometry-Aware Segmentations of Imagescited by 1
- 2025Learning over von Mises-Fisher Distributions via a Wasserstein-like Geometrycited by 1
- 2026Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learningcited by 1
- 2026Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning30 Apr 2026
- 2025Learning over von Mises-Fisher Distributions via a Wasserstein-like Geometry19 Apr 2025
- 2024Probabilistic U-Net with Kendall Shape Spaces for Geometry-Aware Segmentations of Images17 Oct 2024
- 2024Dynamic Feature Learning and Matching for Class-Incremental Learning14 May 2024
- 2022Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-tailed Learning22 Aug 2022