Clusterpath: An Algorithm for Clustering Using Convex Fusion Penalties
Abstract
We present a new clustering algorithm by proposing a convex relaxation of hierarchical clustering, which results in a family of objective functions with a natural geometric interpretation. We give efficient algorithms for calculating the continuous regularization path of solutions, and discuss relative advantages of the parameters. Our method experimentally gives state-of-the-art results similar to spectral clustering for non-convex clusters, and has the added benefit of learning a tree structure from the data.
Cite
Text
Hocking et al. "Clusterpath: An Algorithm for Clustering Using Convex Fusion Penalties." International Conference on Machine Learning, 2011.Markdown
[Hocking et al. "Clusterpath: An Algorithm for Clustering Using Convex Fusion Penalties." International Conference on Machine Learning, 2011.](https://mlanthology.org/icml/2011/hocking2011icml-clusterpath/)BibTeX
@inproceedings{hocking2011icml-clusterpath,
title = {{Clusterpath: An Algorithm for Clustering Using Convex Fusion Penalties}},
author = {Hocking, Toby and Vert, Jean-Philippe and Bach, Francis R. and Joulin, Armand},
booktitle = {International Conference on Machine Learning},
year = {2011},
pages = {745-752},
url = {https://mlanthology.org/icml/2011/hocking2011icml-clusterpath/}
}