Matrix-Regularized Multiple Kernel Learning via $(r,~p)$ Norms

This paper examines a matrix-regularized multiple kernel learning (MKL) technique based on a notion of (r, p) norms. For the problem of learning a linear combination in the support vector machine-based framework, model complexity is typically controlled using various regularization strategies on the combined kernel weights. Recent research has developed a generalized ℓ
_p
-norm MKL framework with tunable variable p(p ≥ 1) to support controlled intrinsic sparsity. Unfortunately, this “1-D” vector ℓ
_p
-norm hardly exploits potentially useful information on how the base kernels “interact.” To allow for higher order kernel-pair relationships, we extend the “1-D” vector ℓ
_p
-MKL to the “2-D” matrix (r, p) norms (1 ≤ r, p <; ∞). We develop a new formulation and an efficient optimization strategy for (r, p)-MKL with guaranteed convergence. A theoretical analysis and experiments on seven UCI data sets shed light on the superiority of (r, p)-MKL over ℓ
_p
-MKL in various scenarios.