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NIPS
2008
15 years 3 months ago
Spectral Clustering with Perturbed Data
Spectral clustering is useful for a wide-ranging set of applications in areas such as biological data analysis, image processing and data mining. However, the computational and/or...
Ling Huang, Donghui Yan, Michael I. Jordan, Nina T...
CORR
2010
Springer
249views Education» more  CORR 2010»
15 years 2 months ago
Performance Analysis of Spectral Clustering on Compressed, Incomplete and Inaccurate Measurements
Spectral clustering is one of the most widely used techniques for extracting the underlying global structure of a data set. Compressed sensing and matrix completion have emerged a...
Blake Hunter, Thomas Strohmer
PAMI
2011
14 years 9 months ago
Parallel Spectral Clustering in Distributed Systems
Spectral clustering algorithms have been shown to be more effective in finding clusters than some traditional algorithms such as k-means. However, spectral clustering suffers fro...
Wen-Yen Chen, Yangqiu Song, Hongjie Bai, Chih-Jen ...
144
Voted
KDD
2010
ACM
245views Data Mining» more  KDD 2010»
15 years 5 months ago
Flexible constrained spectral clustering
Constrained clustering has been well-studied for algorithms like K-means and hierarchical agglomerative clustering. However, how to encode constraints into spectral clustering rem...
Xiang Wang, Ian Davidson
NA
2007
147views more  NA 2007»
15 years 1 months ago
An adaptive spectral least-squares scheme for the Burgers equation
A least-squares spectral collocation method for the one-dimensional inviscid Burgers equation is proposed. This model problem shows the stability and high accuracy of these scheme...
Wilhelm Heinrichs