Iterative Spectral Method for Alternative Clustering
Machine Learning
2019-09-10 v1 Machine Learning
Applications
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.
Keywords
Cite
@article{arxiv.1909.03441,
title = {Iterative Spectral Method for Alternative Clustering},
author = {Chieh Wu and Stratis Ioannidis and Mario Sznaier and Xiangyu Li and David Kaeli and Jennifer G. Dy},
journal= {arXiv preprint arXiv:1909.03441},
year = {2019}
}