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Friday, May 18
Computational Statistics
Invitation to Statistical Analysis and Data Mining
Fri, May 18, 3:30 PM - 5:00 PM
Grand Ballroom E

Flexible Supervised Learning Techniques for Block-missing Data (304754)

*Yufeng Liu, University of North Carolina at Chapel Hill 

Keywords: Block missing data, kernel learning, high dimensions, multi-modality data, regularization, sparsity

Supervised learning techniques have been widely used in diverse scientific disciplines such as biology and neuroscience. In this talk, I will present new techniques 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. New techniques effectively using all available data information without imputation will be discussed. Real applications will be used to illustrate the performance of the proposed methods.