  Kernel approximation methods create explicit, low-dimensional kernel feature
maps to deal with the high computational and memory complexity of standard
techniques. This work studies a supervised kernel learning methodology to
optimize such mappings. We utilize the Discriminant Information criterion, a
measure of class separability with a strong connection to Discriminant
Analysis. By generalizing this measure to cover a wider range of kernel maps
and learning settings, we develop scalable methods to learn kernel features
with high discriminant power. Experimental results on several datasets showcase
that our techniques can improve optimization and generalization performances
over state of the art kernel learning methods.
