Generalized linear multiple kernel learning for high-dimensional image data

We propose GLIMARK, a sparse generalized linear multiple-kernel framework for high-dimensional structured predictors. It extends additive kernel learning to exponential-family outcomes, supports multi-view and hierarchical feature representations, improves scalability via Nyström approximation, and yields interpretable feature-kernel and representer selection.