A Hybrid Algorithm for Convex Semidefinite Optimization
Machine Learning
2012-06-22 v1 Data Structures and Algorithms
Numerical Analysis
Machine Learning
Abstract
We present a hybrid algorithm for optimizing a convex, smooth function over the cone of positive semidefinite matrices. Our algorithm converges to the global optimal solution and can be used to solve general large-scale semidefinite programs and hence can be readily applied to a variety of machine learning problems. We show experimental results on three machine learning problems (matrix completion, metric learning, and sparse PCA) . Our approach outperforms state-of-the-art algorithms.
Cite
@article{arxiv.1206.4608,
title = {A Hybrid Algorithm for Convex Semidefinite Optimization},
author = {Soeren Laue},
journal= {arXiv preprint arXiv:1206.4608},
year = {2012}
}
Comments
ICML2012