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» A Subspace Kernel for Nonlinear Feature Extraction
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CVPR
2010
IEEE
13 years 10 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
CEC
2005
IEEE
13 years 8 months ago
Nonlinear mapping using particle swarm optimisation
Abstract— Nonlinear mapping is an approach of multidimensional scaling where a high-dimensional space is transformed into a lower-dimensional space such that the topological char...
Auralia I. Edwards, Andries Petrus Engelbrecht, Ne...
CVPR
2001
IEEE
14 years 8 months ago
Constructing Facial Identity Surfaces in a Nonlinear Discriminating Space
Recognising face with large pose variation is more challenging than that in a fixed view, e.g. frontal-view, due to the severe non-linearity caused by rotation in depth, selfshadi...
Yongmin Li, Shaogang Gong, Heather M. Liddell
ICANN
1997
Springer
13 years 10 months ago
Kernel Principal Component Analysis
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can e ciently compute principal comp...
Bernhard Schölkopf, Alex J. Smola, Klaus-Robe...
ICMCS
2005
IEEE
182views Multimedia» more  ICMCS 2005»
13 years 11 months ago
An integrated approach for generic object detection using kernel PCA and boosting
In this paper we present a novel framework for generic object class detection by integrating Kernel PCA with AdaBoost. The classifier obtained in this way is invariant to changes...
Saad Ali, Mubarak Shah