Clustering Using Max-Norm Constrained Optimization
Abstract
We suggest using the max-norm as a convex surrogate constraint for clustering. We show how this yields a better exact cluster recovery guarantee than previously suggested nuclear-norm relaxation, and study the effectiveness of our method, and other related convex relaxations, compared to other clustering approaches.
Cite
Text
Jalali and Srebro. "Clustering Using Max-Norm Constrained Optimization." International Conference on Machine Learning, 2012.Markdown
[Jalali and Srebro. "Clustering Using Max-Norm Constrained Optimization." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/jalali2012icml-clustering/)BibTeX
@inproceedings{jalali2012icml-clustering,
title = {{Clustering Using Max-Norm Constrained Optimization}},
author = {Jalali, Ali and Srebro, Nathan},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/jalali2012icml-clustering/}
}