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.