Title: New supervised learning techniques with applications to neuroimaging data

 

Abstract: Supervised learning techniques have been widely used in diverse scientific disciplines such as biology, genetics, and neuroscience. In this talk, I will present some new techniques for flexible learning of data with complex structure. For the first part of the talk, a new efficient regularization technique incorporating graphical structure information among predictors will be introduced. A latent group lasso penalty is applied to utilize the graph structure node-by-node. For the second part of the talk, we focus on data with multiple modalities (sources or types). In practice, it is common to have block-missing structure for such multi-modality data. A new technique effectively using all available data information without imputation will be discussed. Finally, applications for the Alzheimer's Disease Neuroimaging Initiative (ADNI) data will be used to illustrate the performance of these methods.