Multiple Kernel Learning: A Linear Combination Model for Kernel Fusion with Computational Efficiency Enhancements

Multiple kernel learning integrates multiple kernels through linear combination, often hindered by slow optimization due to explicit kernel computations. We propose explicit approximation of kernel mapping functions in finite-dimensional spaces, employing dual coordinate descent for SVM optimization with group Lasso regularization for kernel weights.