ICML 2018oral43 citations
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Heinrich Jiang, Jennifer Jang, Samory Kpotufe
Abstract
We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data. Such seedings act as more stable and expressive cluster-cores than the singleton modes found by Quick Shift. We establish statistical consistency guarantees for this modification. We then show strong clustering performance on real datasets as well as promising applications to image segmentation.
BibTeX
@InProceedings{pmlr-v80-jiang18b,
title = {Quickshift++: Provably Good Initializations for Sample-Based Mean Shift},
author = {Jiang, Heinrich and Jang, Jennifer and Kpotufe, Samory},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {2294--2303},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/jiang18b/jiang18b.pdf},
url = {https://proceedings.mlr.press/v80/jiang18b.html},
abstract = {We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data. Such seedings act as more stable and expressive cluster-cores than the singleton modes found by Quick Shift. We establish statistical consistency guarantees for this modification. We then show strong clustering performance on real datasets as well as promising applications to image segmentation.}
}