Convex Learning with Invariances
Choon Hui Teo, Sam T. Roweis, Amir Globerson, Alex J. Smola · 2007
Incorporating invariances into a learning algorithm is a common problem in ma-chine learning. We provide a convex formulation which can deal with arbitrary loss functions and arbitrary losses. In addition, it is a drop-in replacement for most optimization algorithms for kernels, including solvers of the SVMStruct family. The advantage of our setting is that it relies on column generation instead of mod-ifying the underlying optimization problem directly. 1