Multiresolution Mixture Modeling using Merging of Mixture Components
Proceedings of the Asian Conference on Machine Learning, PMLR 25:17-32, 2012.
Observing natural phenomena at several levels of detail results in multiresolution data. Extending models and algorithms to cope with multiresolution data is a prerequisite for wide-spread exploitation of the data represented in the multiple resolutions. Mixture models are widely used probabilistic models, however, the mixture models in their standard form can be used to analyze the data represented in a single resolution. In this paper, we propose a multiresolution mixture model based on merging of the mixture components across models represented in different resolutions. Result of such an analysis scenario is to have multiple mixture models, one mixture model for each resolution of data. Our proposed solution is based on the idea on the interaction between mixture models. More specifically, we repeatedly merge component distributions of mixture models across different resolutions. We experiment our proposed algorithm on the two real-world chromosomal aberration datasets represented in two different resolutions. Results show an improvement on the compared multiresolution settings.