Supervised and unsupervised model-based clustering with variable selection
The thesis tackles the problem of uncovering hidden structures in high-dimensional data in the presence of noise and non informative variables. It proposes a supervised and an unsupervised mixture models that select the relevant variables and are robust to measurement errors and outliers. Within the...
| Main Author: | |
|---|---|
| Other Authors: | |
| Published: |
Imperial College London
2012
|
| Subjects: | |
| Online Access: | http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.560758 |