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CVPR
2008
IEEE
15 years 26 days ago
Parameterized Kernel Principal Component Analysis: Theory and applications to supervised and unsupervised image alignment
Parameterized Appearance Models (PAMs) (e.g. eigentracking, active appearance models, morphable models) use Principal Component Analysis (PCA) to model the shape and appearance of...
Fernando De la Torre, Minh Hoai Nguyen
IJCNN
2006
IEEE
14 years 4 months ago
Nonlinear principal component analysis of noisy data
With very noisy data, having plentiful samples eliminates overfitting in nonlinear regression, but not in nonlinear principal component analysis (NLPCA). To overcome this problem...
William W. Hsieh
ICDM
2006
IEEE
225views Data Mining» more  ICDM 2006»
14 years 4 months ago
Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples ar...
Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen
ICPR
2006
IEEE
14 years 4 months ago
Regularized Locality Preserving Learning of Pre-Image Problem in Kernel Principal Component Analysis
In this paper, we address the pre-image problem in kernel principal component analysis (KPCA). The preimage problem finds a pattern as the pre-image of a feature vector defined in...
Weishi Zheng, Jian-Huang Lai
ISNN
2009
Springer
14 years 5 months ago
Nonlinear Component Analysis for Large-Scale Data Set Using Fixed-Point Algorithm
Abstract. Nonlinear component analysis is a popular nonlinear feature extraction method. It generally uses eigen-decomposition technique to extract the principal components. But th...
Weiya Shi, Yue-Fei Guo