Learning Spectral Clustering
Abstract
Spectral clustering refers to a class of techniques which rely on the eigen- structure of a similarity matrix to partition points into disjoint clusters with points in the same cluster having high similarity and points in dif- ferent clusters having low similarity. In this paper, we derive a new cost function for spectral clustering based on a measure of error between a given partition and a solution of the spectral relaxation of a minimum normalized cut problem. Minimizing this cost function with respect to the partition leads to a new spectral clustering algorithm. Minimizing with respect to the similarity matrix leads to an algorithm for learning the similarity matrix. We develop a tractable approximation of our cost function that is based on the power method of computing eigenvectors.
Cite
Text
Bach and Jordan. "Learning Spectral Clustering." Neural Information Processing Systems, 2003.Markdown
[Bach and Jordan. "Learning Spectral Clustering." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/bach2003neurips-learning/)BibTeX
@inproceedings{bach2003neurips-learning,
title = {{Learning Spectral Clustering}},
author = {Bach, Francis R. and Jordan, Michael I.},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {305-312},
url = {https://mlanthology.org/neurips/2003/bach2003neurips-learning/}
}