Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition.
Zhao ZhangMing-Bo ZhaoTommy W. S. ChowPublished in: Neural Networks (2012)
Keyphrases
- dimensionality reduction
- semi supervised
- manifold learning
- diffusion maps
- low dimensional
- graph embedding
- semi supervised clustering
- subspace learning
- feature extraction
- pairwise constraints
- nonlinear dimensionality reduction
- pattern recognition
- high dimensional
- recognition rate
- object recognition
- linear projection
- semi supervised dimensionality reduction
- unsupervised learning
- semi supervised learning
- metric learning
- constrained clustering
- lower dimensional
- label information
- supervised learning
- high dimensionality
- locally linear embedding
- principal component analysis
- manifold regularization
- recognition algorithm
- kernel learning
- high dimensional data
- recognition accuracy
- feature space
- data representation
- manifold structure
- feature selection
- unlabeled data
- labeled data
- multi view
- data points
- pairwise
- image processing
- principal components
- dimensionality reduction methods
- active learning
- constraint sets
- geodesic distance
- linear discriminant analysis
- euclidean distance
- action recognition
- clustering method
- computer vision