Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/20792
Title: | Non-Gaussian Mixture Model Averaging for Clustering |
Authors: | Zhang, Xu Xuan |
Advisor: | McNicholas, Paul D. |
Department: | Mathematics and Statistics |
Keywords: | Model-based Clustering, Model Averaging, Mixture Models |
Publication Date: | 2017 |
Abstract: | The Gaussian mixture model has been used for model-based clustering analysis for decades. Most model-based clustering analyses are based on the Gaussian mixture model. Model averaging approaches for Gaussian mixture models are proposed by Wei and McNicholas, based on a family of 14 Gaussian parsimonious clustering models. In this thesis, we use non-Gaussian mixture models, namely the tEigen family, for our averaging approaches. This paper studies fitting in an averaged model from a set of multivariate t-mixture models instead of fitting a best model. |
URI: | http://hdl.handle.net/11375/20792 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Zhang_XuXuan_2016Nov_MSc.pdf | 274.16 kB | Adobe PDF | View/Open |
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.