Feature space distance metric learning for discriminant graph embedding.
Bo LiZhang-Tao FanXiaolong ZhangPublished in: IJCNN (2016)
Keyphrases
- graph embedding
- distance metric learning
- low dimensional
- feature space
- metric learning
- nonlinear dimensionality reduction
- dimensionality reduction
- discriminant analysis
- semi supervised
- principal component analysis
- high dimensional
- distance metric
- linear discriminant analysis
- manifold learning
- data points
- feature extraction
- high dimensional data
- input space
- image classification
- dimension reduction
- feature vectors
- subspace learning
- euclidean distance
- kernel function
- euclidean space
- kernel matrix
- training samples
- face recognition
- classification accuracy
- hyperplane
- image representation
- data representation
- locally linear embedding
- feature selection
- dimensionality reduction methods
- sparse coding
- semi supervised learning
- input data
- feature set
- image retrieval
- kernel methods
- supervised learning
- multiple features
- prior knowledge
- learning process
- pairwise
- training set
- pattern recognition
- data sets