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» Approximate Kernel Clustering
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ICML
2006
IEEE
15 years 10 months ago
Practical solutions to the problem of diagonal dominance in kernel document clustering
In supervised kernel methods, it has been observed that the performance of the SVM classifier is poor in cases where the diagonal entries of the Gram matrix are large relative to ...
Derek Greene, Padraig Cunningham
77
Voted
NIPS
2007
14 years 11 months ago
Discriminative K-means for Clustering
We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and cluster...
Jieping Ye, Zheng Zhao, Mingrui Wu
PR
2010
156views more  PR 2010»
14 years 8 months ago
Semi-supervised clustering with metric learning: An adaptive kernel method
Most existing representative works in semi-supervised clustering do not sufficiently solve the violation problem of pairwise constraints. On the other hand, traditional kernel met...
Xuesong Yin, Songcan Chen, Enliang Hu, Daoqiang Zh...
68
Voted
ICPR
2002
IEEE
15 years 2 months ago
A Large Scale Clustering Scheme for Kernel K-Means
Kernel functions can be viewed as a non-linear transformation that increases the separability of the input data by mapping them to a new high dimensional space. The incorporation ...
Rong Zhang, Alexander I. Rudnicky
KDD
2004
ACM
190views Data Mining» more  KDD 2004»
15 years 10 months ago
Kernel k-means: spectral clustering and normalized cuts
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis