  In the recent past, automatic selection or combination of kernels (or
features) based on multiple kernel learning (MKL) approaches has been receiving
significant attention from various research communities. Though MKL has been
extensively studied in the context of support vector machines (SVM), it is
relatively less explored for ratio-trace problems. In this paper, we show that
MKL can be formulated as a convex optimization problem for a general class of
ratio-trace problems that encompasses many popular algorithms used in various
computer vision applications. We also provide an optimization procedure that is
guaranteed to converge to the global optimum of the proposed optimization
problem. We experimentally demonstrate that the proposed MKL approach, which we
refer to as MKL-RT, can be successfully used to select features for
discriminative dimensionality reduction and cross-modal retrieval. We also show
that the proposed convex MKL-RT approach performs better than the recently
proposed non-convex MKL-DR approach.
