Mixture of Watson Distributions: A Generative Model for Hyperspherical Embeddings
Avleen S. Bijral, Markus Breitenbach, Greg Grudic;
JMLR W&P 2:35-42, 2007.
Machine learning applications often involve data that can be analyzed as unit vectors on a d-dimensional hypersphere, or equivalently are directional in nature. Spectral clustering techniques generate embeddings that constitute an example of directional data and can result in different shapes on a hypersphere (depending on the original structure). Other examples of directional data include text and some sub-domains of bioinformatics. The Watson distribution for directional data presents a tractable form and has more modeling capability than the simple von Mises-Fisher distribution. In this paper, we present a generative model of mixtures of Watson distributions on a hypersphere and derive numerical approximations of the parameters in an Expectation Maximization (EM) setting. This model also allows us to present an explanation for choosing the right embedding dimension for spectral clustering. We analyze the algorithm on a generated example and demonstrate its superiority over the existing algorithms through results on real datasets.