Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/22133
Title: | Clustering Matrix Variate Data Using Finite Mixture Models with Component-Wise Regularization |
Authors: | Tait, Peter A |
Advisor: | McNicholas, Paul D |
Department: | Statistics |
Keywords: | Mixture models;Matrix variate distributions;Accelerometers;Pediatrics |
Publication Date: | Nov-2017 |
Abstract: | Matrix variate distributions present a innate way to model random matrices. Realiza- tions of random matrices are created by concurrently observing variables in different locations or at different time points. We use a finite mixture model composed of matrix variate normal densities to cluster matrix variate data. The matrix variate data was generated by accelerometers worn by children in a clinical study conducted at McMaster. Their acceleration along the three planes of motion over the course of seven days, forms their matrix variate data. We use the resulting clusters to verify existing group membership labels derived from a test of motor-skills proficiency used to assess the children’s locomotion. |
URI: | http://hdl.handle.net/11375/22133 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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tait_peter_a_201709_msc.pdf | MSc thesis as a pdf file | 1.02 MB | Adobe PDF | View/Open |
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