Kernel principal component analysis
Advances in kernel methods
AI Game Programming Wisdom
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel Nearest-Neighbor Algorithm
Neural Processing Letters
Local Binary Pattern Descriptors for Dynamic Texture Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Face Recognition Using Kernel PCA and Hierarchical RBF Network
CISIM '07 Proceedings of the 6th International Conference on Computer Information Systems and Industrial Management Applications
Polynomial and RBF Kernels as Marker Selection Tools-A Breast Cancer Case Study
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Kernel Methods in Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Isolated Word Recognition Using Low Dimensional Features and Kernel Based Classification
ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Mercer’s theorem, feature maps, and smoothing
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Learning pattern classification-a survey
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
In this paper, a new technique is presented to measure dissimilarity in kernel space providing scaling and translation invariance. The motivation comes from signal/image processing, where classifiers are often required to ensure invariance against linear transforms, since in many cases linear transforms do not affect the content of a signal/image for a human observer. We examine the theoretical background of linear invariance in the polynomial kernel space, introduce the centered correlation and centered Euclidean dissimilarity in kernel space, deduce formulas to compute it efficiently and test the proposed dissimilarity measures with the kNN classifier. The experimental results show that the presented techniques are highly competitive in similarity or dissimilarity based classification methods.