MARK: a boosting algorithm for heterogeneous kernel models
Support Vector Machines and other kernel methods have proven to be very effective for nonlinear inference. Practical issues are how to select the type of kernel including any parameters and how to deal with the computational issues caused by the fact that the kernel matrix grows quadratically with the data. Inspired by ensemble and boosting methods like MART, we propose the Multiple Additive Regression Kernels (MARK) algorithm to address these issues. MARK considers a large (potentially infinite) library of kernel matrices formed by different kernel functions and parameters. Using gradient boosting/column generation, MARK constructs columns of the heterogeneous kernel matrix (the base hypotheses) on the fly and then adds them into the kernel ensemble. Regularization methods such as used in SVM, kernel ridge regression, and MART, are used to prevent overfitting. We investigate how MARK is applied to heterogeneous kernel ridge regression. The resulting algorithm is simple to implement and efficient. Kernel parameter selection is handled within MARK. Sampling and "weak" kernels are used to further enhance the computational efficiency of the resulting additive algorithm. The user can incorporate and potentially extract domain knowledge by restricting the kernel library to interpretable kernels. MARK compares very favorably with SVM and kernel ridge regression on several benchmark datasets.