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» A Tutorial on Spectral Clustering
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NIPS
2008
13 years 8 months ago
Learning Taxonomies by Dependence Maximization
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the ...
Matthew B. Blaschko, Arthur Gretton
MVA
2000
122views Computer Vision» more  MVA 2000»
13 years 8 months ago
Unsupervised Classification of X-Ray Mapping Images of Polished Sections
X-ray mapping images of polished sections are classified using two unsupervised clustering algorithms. The methods applied are the k-means algorithm and an extended spectral fuzzy...
Klaus Baggesen Hilger, Allan Aasbjerg Nielsen, Jen...
AUSAI
2009
Springer
13 years 10 months ago
Adapting Spectral Co-clustering to Documents and Terms Using Latent Semantic Analysis
Abstract. Spectral co-clustering is a generic method of computing coclusters of relational data, such as sets of documents and their terms. Latent semantic analysis is a method of ...
Laurence A. F. Park, Christopher Leckie, Kotagiri ...
IJCAI
2003
13 years 8 months ago
Spectral Learning
We present a simple, easily implemented spectral learning algorithm which applies equally whether we have no supervisory information, pairwise link constraints, or labeled example...
Sepandar D. Kamvar, Dan Klein, Christopher D. Mann...
ICIP
2007
IEEE
14 years 9 months ago
Abnormal Event Detection from Surveillance Video by Dynamic Hierarchical Clustering
The clustering-based approach for detecting abnormalities in surveillance video requires the appropriate definition of similarity between events. The HMM-based similarity defined ...
Fan Jiang, Ying Wu, Aggelos K. Katsaggelos