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» Approximate Kernel Clustering
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PAKDD
2005
ACM
142views Data Mining» more  PAKDD 2005»
15 years 3 months ago
Dynamic Cluster Formation Using Level Set Methods
Density-based clustering has the advantages for (i) allowing arbitrary shape of cluster and (ii) not requiring the number of clusters as input. However, when clusters touch each o...
Andy M. Yip, Chris H. Q. Ding, Tony F. Chan
SDM
2004
SIAM
218views Data Mining» more  SDM 2004»
14 years 11 months ago
Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data
This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...
Ashok N. Srivastava
NN
2006
Springer
128views Neural Networks» more  NN 2006»
14 years 9 months ago
Topographic map formation of factorized Edgeworth-expanded kernels
We introduce a new learning algorithm for topographic map formation of Edgeworth-expanded Gaussian activation kernels. In order to avoid the rapid increase in kernel parameters, a...
Marc M. Van Hulle
ICPR
2008
IEEE
15 years 4 months ago
Object recognition using graph spectral invariants
Graph structures have been proved important in high level-vision since they can be used to represent structural and relational arrangements of objects in a scene. One of the probl...
Bai Xiao, Richard C. Wilson, Edwin R. Hancock
MLDM
2007
Springer
15 years 3 months ago
Kernel MDL to Determine the Number of Clusters
In this paper we propose a new criterion, based on Minimum Description Length (MDL), to estimate an optimal number of clusters. This criterion, called Kernel MDL (KMDL), is particu...
Ivan O. Kyrgyzov, Olexiy O. Kyrgyzov, Henri Ma&ici...