Unsupervised feature extraction via kernel subspace techniques.
Ana R. TeixeiraAna Maria ToméElmar Wolfgang LangPublished in: Neurocomputing (2011)
Keyphrases
- feature extraction
- feature space
- discriminant analysis
- discriminant subspace
- discriminant projection
- principal component analysis
- dimensionality reduction
- kernel pca
- kernel based nonlinear
- kernel principal component analysis
- kernel function
- feature vectors
- principle component analysis
- low dimensional
- feature selection
- kernel methods
- wavelet transform
- preprocessing
- face recognition
- unsupervised learning
- pattern classification
- frequency domain
- dimension reduction
- kernel space
- image classification
- feature set
- supervised learning
- semi supervised
- principal components
- texture analysis
- dimensionality reduction methods
- subspace learning
- feature representation
- image processing
- pattern recognition
- linear discriminant analysis
- support vector
- input space
- high dimensional
- dot product
- principal components analysis
- high dimensional data
- data points
- support vector machine svm
- kernel matrix
- extracted features
- gabor filters
- similarity function
- high dimensional feature space
- input data
- support vector machine
- texture features
- manifold learning