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Activity Number: 104
Type: Invited
Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #318157
Title: Sparse Regression for Block Missing Data Without Imputation
Author(s): Yufeng Liu*
Companies: The University of North Carolina at Chapel Hill
Keywords: Block missing ; Regression ; Sparsity

Supervised learning techniques have been widely used in diverse scientific disciplines such as 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.

Authors who are presenting talks have a * after their name.

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