Regularized Multivariate von Mises Distribution

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 L1 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.