Unsupervised spectral learning
Machine Learning
2012-07-09 v1 Machine Learning
Abstract
In spectral clustering and spectral image segmentation, the data is partioned starting from a given matrix of pairwise similarities S. the matrix S is constructed by hand, or learned on a separate training set. In this paper we show how to achieve spectral clustering in unsupervised mode. Our algorithm starts with a set of observed pairwise features, which are possible components of an unknown, parametric similarity function. This function is learned iteratively, at the same time as the clustering of the data. The algorithm shows promosing results on synthetic and real data.
Cite
@article{arxiv.1207.1358,
title = {Unsupervised spectral learning},
author = {Susan Shortreed and Marina Meila},
journal= {arXiv preprint arXiv:1207.1358},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005)