Application of SsVGMM to medical data-classification with novelty detection (A semi-supervised variational Gaussian mixture model)
@inproceedings{yang2017application, title={Application of SsVGMM to medical data-classification with novelty detection}, author={Yang, Fan and Soriano, Jaymar and Kubo, Takatomi and Ikeda, Kazushi}, booktitle={Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE}, pages={3098--3101}, year={2017}, organization={IEEE} }
This program can be easily understood from the demo.m file. A dynamic constraint is used in the variational inference, so that a semi-supervised learning can be performed.