Unsupervised Spectral 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
Text
Shortreed and Meila. "Unsupervised Spectral Learning." Conference on Uncertainty in Artificial Intelligence, 2005.Markdown
[Shortreed and Meila. "Unsupervised Spectral Learning." Conference on Uncertainty in Artificial Intelligence, 2005.](https://mlanthology.org/uai/2005/shortreed2005uai-unsupervised/)BibTeX
@inproceedings{shortreed2005uai-unsupervised,
title = {{Unsupervised Spectral Learning}},
author = {Shortreed, Susan M. and Meila, Marina},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2005},
pages = {534-541},
url = {https://mlanthology.org/uai/2005/shortreed2005uai-unsupervised/}
}