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Spectral clustering

In multivariate statistics and the clustering of data, spectral clustering techniques make use of the spectrum (eigenvalues) of the similarity matrix… Expand
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Highly Cited
2011
Highly Cited
2011
In many clustering problems, we have access to multiple views of the data each of which could be individually used for clustering… Expand
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Highly Cited
2011
Highly Cited
2011
We propose a spectral clustering algorithm for the multi-view setting where we have access to multiple views of the data, each of… Expand
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Highly Cited
2008
Highly Cited
2008
Spectral clustering and path-based clustering are two recently developed clustering approaches that have delivered impressive… Expand
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Highly Cited
2005
Highly Cited
2005
Clustering nodes in a graph is a useful general technique in data mining of large network data sets. In this context, Newman and… Expand
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Highly Cited
2004
Highly Cited
2004
We study a number of open issues in spectral clustering: (i) Selecting the appropriate scale of analysis, (ii) Handling multi… Expand
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Highly Cited
2004
Highly Cited
2004
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space… Expand
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Highly Cited
2003
Highly Cited
2003
  • S. Yu, J. Shi
  • Proceedings Ninth IEEE International Conference…
  • 2003
  • Corpus ID: 2196967
We propose a principled account on multiclass spectral clustering. Given a discrete clustering formulation, we first solve a… Expand
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Highly Cited
2003
Highly Cited
2003
Several unsupervised learning algorithms based on an eigendecomposition provide either an embedding or a clustering only for… Expand
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Highly Cited
2003
Highly Cited
2003
Spectral clustering refers to a class of techniques which rely on the eigen-structure of a similarity matrix to partition points… Expand
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Highly Cited
2001
Highly Cited
2001
Despite many empirical successes of spectral clustering methods— algorithms that cluster points using eigenvectors of matrices… Expand
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