Discriminant Low-dimensional Subspace Analysis for Face Recognition with Small Number of Training Samples.
Hui KongXuchun LiJian-Gang WangEam Khwang TeohChandra KambhamettuPublished in: BMVC (2005)
Keyphrases
- subspace analysis
- low dimensional
- small number of training samples
- face recognition
- high dimensional
- kernel principal component analysis
- principal component analysis
- dimensionality reduction
- subspace learning
- linear subspace
- high dimensional data
- subspace methods
- manifold learning
- dimension reduction
- lower dimensional
- data points
- feature space
- linear discriminant analysis
- kernel pca
- face images
- input space
- discriminant analysis
- sparse representation
- high dimensional feature space
- kernel function
- recognition rate