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Kernel PCA and De-Noising in Feature Spaces.
Sebastian Mika
Bernhard Schölkopf
Alexander J. Smola
Klaus-Robert Müller
Matthias Scholz
Gunnar Rätsch
Published in:
NIPS (1998)
Keyphrases
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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