Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression.
Roman RosipalMark A. GirolamiLeonard J. TrejoAndrzej CichockiPublished in: Neural Comput. Appl. (2001)
Keyphrases
- kernel pca
- nonlinear regression
- feature extraction
- denoising
- kernel principal component analysis
- support vector machine
- principal component analysis
- linear regression
- support vector regression
- feature space
- kernel methods
- image processing
- dimensionality reduction
- linear discriminant analysis
- kernel function
- face recognition
- regression method
- feature vectors
- preprocessing
- support vector machine svm
- discriminant analysis
- pattern recognition
- kernel matrix
- image denoising
- regression methods
- support vector
- regression problems
- spectral clustering
- dimension reduction
- wavelet transform
- gabor wavelets
- extracted features
- machine learning
- input space
- least squares
- gabor filters
- independent component analysis
- texture features
- svm classifier
- low dimensional
- classification accuracy
- feature selection
- data sets