@article {citation210,
title = {Learning Spectral Clustering, WIth Application To Speech Separation},
journal = {Journal of Machine Learning Research 7},
year = {2006},
month = {10/2006},
abstract = {Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity
matrix to partition points into disjoint clusters, with points in the same cluster having high similarity
and points in different clusters having low similarity. In this paper, we derive new cost functions
for spectral clustering based on measures of error between a given partition and a solution of the
spectral relaxation of a minimum normalized cut problem. Minimizing these cost functions with
respect to the partition leads to new spectral clustering algorithms. Minimizing with respect to the
similarity matrix leads to algorithms for learning the similarity matrix from fully labelled data sets.
We apply our learning algorithm to the blind one-microphone speech separation problem, casting
the problem as one of segmentation of the spectrogram.
},
url = {http://www.cs.berkeley.edu/~jordan/papers/sgp-jmlr.pdf},
author = {Francis R. Bach and Michael I. Jordan}
}