Recursively global and local discriminant analysis for semi-supervised and unsupervised dimension reduction with image analysis.
Shangbing GaoJun ZhouYunyang YanQiaolin YePublished in: Neurocomputing (2016)
Keyphrases
- dimension reduction
- discriminant analysis
- semi supervised
- linear discriminant analysis
- principal component analysis
- feature extraction
- unsupervised learning
- discriminant projection
- manifold learning
- cluster analysis
- semi supervised learning
- partial least squares
- face recognition
- dimensionality reduction
- supervised learning
- labeled data
- scatter matrices
- support vector
- high dimensional data
- pairwise
- fisher discriminant analysis
- unlabeled data
- subspace learning
- dimension reduction methods
- support vector machine svm
- active learning
- high dimensional
- null space
- random projections
- low dimensional
- pattern recognition
- graph embedding
- dimensionality reduction methods
- computer vision
- feature selection
- image segmentation
- object recognition
- discriminative information
- data sets
- metric learning
- preprocessing
- sparse coding
- independent component analysis
- supervised dimensionality reduction
- text mining
- singular value decomposition