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CVPR
2010
IEEE
13 years 11 months ago
Bayes Optimal Kernel Discriminant Analysis
Kernel methods provide an efficient mechanism to derive nonlinear algorithms. In classification problems as well as in feature extraction, kernel-based approaches map the original...
Di You, Aleix Martinez
COMPGEOM
2011
ACM
12 years 11 months ago
Comparing distributions and shapes using the kernel distance
Starting with a similarity function between objects, it is possible to define a distance metric (the kernel distance) on pairs of objects, and more generally on probability distr...
Sarang C. Joshi, Raj Varma Kommaraju, Jeff M. Phil...
TIP
2011
162views more  TIP 2011»
13 years 2 months ago
Kernel Maximum Autocorrelation Factor and Minimum Noise Fraction Transformations
—This paper introduces kernel versions of maximum autocorrelation factor (MAF) analysis and minimum noise fraction (MNF) analysis. The kernel versions are based upon a dual formu...
Allan Aasbjerg Nielsen
TIT
2008
224views more  TIT 2008»
13 years 7 months ago
Graph-Based Semi-Supervised Learning and Spectral Kernel Design
We consider a framework for semi-supervised learning using spectral decomposition-based unsupervised kernel design. We relate this approach to previously proposed semi-supervised l...
Rie Johnson, Tong Zhang
KDD
2005
ACM
171views Data Mining» more  KDD 2005»
14 years 7 months ago
Application of kernels to link analysis
Abstract. The application of kernel methods to link analysis is explored. We argue that a family of kernels on graphs provides a unified perspective on the three measures proposed ...
Takahiko Ito, Masashi Shimbo, Taku Kudo, Yuji Mats...