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Results 1 - 10
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491,039
Methodologies in spectral analysis of large dimensional random matrices, a review
- STATIST. SINICA
, 1999
"... In this paper, we give a brief review of the theory of spectral analysis of large dimensional random matrices. Most of the existing work in the literature has been stated for real matrices but the corresponding results for the complex case are also of interest, especially for researchers in Electri ..."
Abstract
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Cited by 453 (37 self)
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In this paper, we give a brief review of the theory of spectral analysis of large dimensional random matrices. Most of the existing work in the literature has been stated for real matrices but the corresponding results for the complex case are also of interest, especially for researchers
On Spectral Clustering: Analysis and an algorithm
- ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
, 2001
"... Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors in slightly ..."
Abstract
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Cited by 1697 (13 self)
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Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors
Consistency of spectral clustering
, 2004
"... Consistency is a key property of statistical algorithms, when the data is drawn from some underlying probability distribution. Surprisingly, despite decades of work, little is known about consistency of most clustering algorithms. In this paper we investigate consistency of a popular family of spe ..."
Abstract
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Cited by 567 (15 self)
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under strong additional assumptions, which, as we demonstrate, are not always satisfied in real data. We conclude that our analysis provides strong evidence for the superiority of normalized spectral clustering in practical applications. We believe that methods used in our analysis will provide a basis
SPECTRAL ANALYSIS OF ORIENTED FEATURES IN
, 2015
"... KIMMO KARTASALO: Spectral analysis of oriented features in micrographs ..."
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
- Advances in Neural Information Processing Systems 14
, 2001
"... Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in a higher ..."
Abstract
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Cited by 664 (8 self)
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Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in a higher dimensional space. The algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality preserving properties and a natural connection to clustering. Several applications are considered.
Spectral Analysis for Neural Signals
"... This chapter introduces concepts fundamental to spectral analysis and applies spectral analysis to char-acterize neural signals. Spectral analysis is a form of time series analysis and concerns a series of events ..."
Abstract
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This chapter introduces concepts fundamental to spectral analysis and applies spectral analysis to char-acterize neural signals. Spectral analysis is a form of time series analysis and concerns a series of events
Spectral Analysis of Internet Topologies
, 2003
"... We perform spectral analysis of the Internet topology at the AS level, by adapting the standard spectral filtering method of examining the eigenvectors corresponding to the largest eigenvalues of matrices related to the adjacency matrix of the topology. We observe that the method suggests clusters o ..."
Abstract
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Cited by 86 (6 self)
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We perform spectral analysis of the Internet topology at the AS level, by adapting the standard spectral filtering method of examining the eigenvectors corresponding to the largest eigenvalues of matrices related to the adjacency matrix of the topology. We observe that the method suggests clusters
Results 1 - 10
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491,039
