Sparse Bayesian Multi-Task Learning for Predicting Cognitive Outcomes from Neuroimaging Measures in Alzheimer's Disease.
This study proposed a sparse Bayesian multi-task learning algorithm to improve the prediction accuracy on the cognitive outcomes from neuroimaging measures in Alzheimer's disease. A variant of T-MSBL was proposed, and its connection to existing algorithms in this field was established, showing the advantages of the T-MSBL family. We achieved the highest prediction accuracy, compared to the latest results published in top journals in 2011.
I will introduce the paper in details in my next post. The camera-ready can be downloaded from here, and the code will be posted soon.
