Maximum-Margin Matrix Factorization

Nathan Srebro, Jason D. M. Rennie, Tommi S. Jaakkola

Abstract: We present a novel approach to collaborative prediction, using low-norm instead of low-rank factorizations. The approach is inspired by, and has strong connections to, large-margin linear discrimination. We show how to learn low-norm factorizations by solving a semi-definite program, and discuss generalization error bounds for them.