Department of Statistics and Operations Research
University of North Carolina at Chapel Hill
Sparse Regression for Block Missing Data Without Imputation
Supervised learning techniques have been widely used in diverse scientific disciplines such as business, finance, biology and neuroscience. In this talk, I will present a new technique for flexible learning of data with complex block-missing structure. 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. Applications for the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data will be used to illustrate the performance of the proposed method.
Friday, 10/19/2018, BLOC 113, 11:30 AM