Low-Rank Tucker Approximation of a Tensor From Streaming Data

2 code implementations • 24 Apr 2019 • Yiming Sun, Yang Guo, Charlene Luo, Joel Tropp, Madeleine Udell

This paper describes a new algorithm for computing a low-Tucker-rank approximation of a tensor.

Practical Large-Scale Optimization for Max-norm Regularization

no code implementations • NeurIPS 2010 • Jason D. Lee, Ben Recht, Nathan Srebro, Joel Tropp, Ruslan R. Salakhutdinov

The max-norm was proposed as a convex matrix regularizer by Srebro et al (2004) and was shown to be empirically superior to the trace-norm for collaborative filtering problems.

Clustering Collaborative Filtering