Convex Learning with Invariances

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Choon Teo, Amir Globerson, Sam Roweis, Alex Smola


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.