Multi-Class Sparse Bayesian Regression for Neuroimaging Data Analysis

The use of machine learning tools is gaining popularity in neuroimaging, as it provides a sensitive assessment of the information conveyed by brain images. In particular, finding regions of the brain whose functional signal reliably predicts some behavioral information makes it possible to better understand how this information is encoded or processed in the brain. However, such a prediction is performed through regression or classification algorithms that suffer from the curse of dimensionality, because a huge number of features (i.e. voxels) are available to fit some target, with very few samples (i.e. scans) to learn the informative regions. A commonly used solution is to regularize the weights of the parametric prediction function. However, model specification needs a careful design to balance adaptiveness and sparsity. In this paper, we introduce a novel method, Multi-Class Sparse Bayesian Regression (MCBR ), that generalizes classical approaches such as Ridge regression and ...

• Type: Journal
• Year: 2010
• Where: MICCAI
• Authors: Vincent Michel, Evelyn Eger, Christine Keribin, Bertrand Thirion
• Keywords: Medical Imaging, MICCAI 2010, Parametric Prediction Function, Prediction, Sparse Bayesian Regression