Fast maximum margin matrix factorization for collaborative prediction

@inproceedings{Rennie2005FastMM,
  title={Fast maximum margin matrix factorization for collaborative prediction},
  author={Jason D. M. Rennie and Nathan Srebro},
  booktitle={ICML},
  year={2005}
}
Maximum Margin Matrix Factorization (MMMF) was recently suggested (Srebro et al., 2005) as a convex, infinite dimensional alternative to low-rank approximations and standard factor models. MMMF can be formulated as a semi-definite programming (SDP) and learned using standard SDP solvers. However, current SDP solvers can only handle MMMF problems on matrices of dimensionality up to a few hundred. Here, we investigate a direct gradient-based optimization method for MMMF and demonstrate it on… CONTINUE READING
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