Kernel PCA and De-Noising in Feature Spaces.
Sebastian MikaBernhard SchölkopfAlexander J. SmolaKlaus-Robert MüllerMatthias ScholzGunnar RätschPublished in: NIPS (1998)
Keyphrases
- kernel pca
- feature space
- denoising
- kernel matrix
- kernel methods
- dimensionality reduction
- laplacian eigenmaps
- image denoising
- kernel principal component analysis
- kernel function
- principal component analysis
- input space
- feature vectors
- feature extraction
- high dimensional feature space
- image processing
- hyperplane
- high dimensional
- training samples
- feature selection
- input data
- classification accuracy
- data points
- feature set
- support vector machine
- linear discriminant analysis
- training set
- distance metric
- model selection
- low dimensional
- face recognition
- least squares
- metric learning
- computer vision
- pairwise
- preprocessing
- data sets