Learning Spectral Clustering, With Application To Speech Separation Export

@article{citeulike:2221782, 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.}, author = {Bach, Francis R. and Jordan, Michael I.}, citeulike-article-id = {2221782}, citeulike-linkout-0 = {http://www.jmlr.org/papers/volume7/bach06b/bach06b.pdf}, journal = {Journal of Machine Learning Research}, keywords = {spectral-clustering, speech-recognition}, month = {October}, pages = {1963--2001}, posted-at = {2008-02-04 03:31:31}, priority = {3}, title = {Learning Spectral Clustering, With Application To Speech Separation}, url = {http://www.jmlr.org/papers/volume7/bach06b/bach06b.pdf}, volume = {7}, year = {2006} }

TY - JOUR ID - citeulike:2221782 L3 - citeulike-article-id:2221782 N2 - 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. JF - Journal of Machine Learning Research EP - 2001 TI - Learning Spectral Clustering, With Application To Speech Separation VL - 7 SP - 1963 KW - spectral-clustering KW - speech-recognition AU - Bach, Francis AU - Jordan, Michael PY - 2006/10// UR - http://www.jmlr.org/papers/volume7/bach06b/bach06b.pdf ER -