Abstract
Regularization is necessary to avoid overfitting when the number of data samples is low compared to the number of parameters of the model. In this paper, we introduce a flexible \(L_1\) regularization for the multivariate von Mises distribution. We also propose a circular distance that can be used to estimate the Kullback-Leibler divergence between two circular distributions by means of sampling, and also serves as goodness-of-fit measure. We compare the models on synthetic data and real morphological data from human neurons and show that the regularized model achieves better results than non regularized von Mises model.
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Acknowledgements
This work has been partially supported by the Spanish Ministry of Economy and Competitiveness through the Cajal Blue Brain (C080020-09; the Spanish partner of the Blue Brain initiative from EPFL) and TIN2013-41592-P projects, by the Regional Government of Madrid through the S2013/ICE-2845-CASI-CAM-CM project, and by the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project).
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Rodriguez-Lujan, L., Bielza, C., Larrañaga, P. (2015). Regularized Multivariate von Mises Distribution. In: Puerta, J., et al. Advances in Artificial Intelligence. CAEPIA 2015. Lecture Notes in Computer Science(), vol 9422. Springer, Cham. https://doi.org/10.1007/978-3-319-24598-0_3
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DOI: https://doi.org/10.1007/978-3-319-24598-0_3
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