Clustering Via Mixture Models presented by Paul McNicholas, Department of Mathematics & Statistics McMaster University
The application of mixture models for clustering has grown into an important subfield of classification. First, the framework for mixture model-based clustering is established and some historical context is provided. Then, some previous work is reviewed before more recent work is presented. This includes work on clustering in the presence of outlying points as well as approaches for asymmetric clusters and high-dimensional data. The talk concludes with a discussion about ongoing and future work, including some work on mixtures of matrix variate distributions.
Mathematics Department, Colloquium Room #319, Chase Building, Dalhousie UniveristyStatistics Seminar