Kernel Self-optimized Locality Preserving Discriminant Analysis for feature extraction and recognition.
Jun-Bao LiJeng-Shyang PanShyi-Ming ChenPublished in: Neurocomputing (2011)
Keyphrases
- discriminant analysis
- feature extraction
- locality preserving
- fisher discriminant analysis
- kernel trick
- dimensionality reduction methods
- face recognition
- manifold learning
- linear discriminant analysis
- dimensionality reduction
- kernel principal component analysis
- feature space
- graph embedding
- kernel pca
- locality preserving projections
- principal component analysis
- embedding space
- image processing
- dimension reduction
- image classification
- pattern recognition
- feature selection
- feature vectors
- fisher criterion
- input space
- principle component analysis
- data sets
- object recognition
- face images
- support vector machine svm
- feature representation
- kernel function
- kernel learning
- principal components analysis
- maximum margin
- data representation
- sparse representation
- similarity measure
- machine learning
- independent component analysis
- kernel methods