Iterative Spectral Method for Alternative Clustering
Abstract
Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition. One of the state-of-the-art approaches is Kernel Dimension Alternative Clustering (KDAC). We propose a novel Iterative Spectral Method (ISM) that greatly improves the scalability of KDAC. Our algorithm is intuitive, relies on easily implementable spectral decompositions, and comes with theoretical guarantees. Its computation time improves upon existing implementations of KDAC by as much as 5 orders of magnitude.
Cite
Text
Wu et al. "Iterative Spectral Method for Alternative Clustering." International Conference on Artificial Intelligence and Statistics, 2018.Markdown
[Wu et al. "Iterative Spectral Method for Alternative Clustering." International Conference on Artificial Intelligence and Statistics, 2018.](https://mlanthology.org/aistats/2018/wu2018aistats-iterative/)BibTeX
@inproceedings{wu2018aistats-iterative,
title = {{Iterative Spectral Method for Alternative Clustering}},
author = {Wu, Chieh and Ioannidis, Stratis and Sznaier, Mario and Li, Xiangyu and Kaeli, David R. and Dy, Jennifer G.},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2018},
pages = {115-123},
url = {https://mlanthology.org/aistats/2018/wu2018aistats-iterative/}
}