Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
The Journal of Machine Learning Research
Learning Overcomplete Representations
Neural Computation
Underdetermined source separation using mixtures of warped laplacians
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Batch and Online Underdetermined Source Separation Using Laplacian Mixture Models
IEEE Transactions on Audio, Speech, and Language Processing
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In this work, a novel probability distribution is proposed to model sparse directional data. The Directional Laplacian Distribution (DLD) is a hybrid between the linear Laplacian distribution and the von Mises distribution, proposed to model sparse directional data. The distribution's parameters are estimated using Maximum-Likelihood Estimation over a set of training data points. Mixtures of Directional Laplacian Distributions (MDLD) are also introduced in order to model multiple concentrations of sparse directional data. The author explores the application of the derived DLD mixtures to cluster sound sources that exist in an underdetermined two-sensor mixture.