Fast maximum margin matrix factorization for collaborative prediction

  title={Fast maximum margin matrix factorization for collaborative prediction},
  author={Jason D. M. Rennie and Nathan Srebro},
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
Highly Influential
This paper has highly influenced 68 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 918 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 584 extracted citations

Matrix Factorization for Collaborative Budget Allocation

IEEE Transactions on Automation Science and Engineering • 2018
View 6 Excerpts
Highly Influenced

Predictive Matrix-Variate t Models

View 7 Excerpts
Highly Influenced

Estimate the Lost Phasor Measurement Unit Data Using Alternating Direction Multipliers Method

2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D) • 2018
View 6 Excerpts
Highly Influenced

919 Citations

Citations per Year
Semantic Scholar estimates that this publication has 919 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.

Similar Papers

Loading similar papers…