Hierarchical discriminant manifold learning for dimensionality reduction and image classification.
Weihai ChenChangchen ZhaoKai DingXingming WuPeter C. Y. ChenPublished in: J. Electronic Imaging (2015)
Keyphrases
- manifold learning
- dimensionality reduction
- image classification
- feature extraction
- low dimensional
- principal component analysis
- locality preserving projections
- discriminant projection
- nonlinear dimensionality reduction
- high dimensional data
- unsupervised learning
- diffusion maps
- discriminant analysis
- high dimensional
- image representation
- sparse representation
- subspace learning
- feature space
- pattern recognition
- dimensionality reduction methods
- bag of words
- linear discriminant analysis
- feature selection
- discriminant embedding
- lower dimensional
- locally linear embedding
- laplacian eigenmaps
- input space
- manifold learning algorithm
- locality preserving
- singular value decomposition
- image features
- data points
- dimension reduction
- high dimensionality
- graph embedding
- semi supervised
- face recognition
- computer vision
- sparse coding
- image processing
- principal components
- low dimensional manifolds
- data sets
- neighborhood preserving embedding
- intrinsic dimensionality
- manifold structure
- principal components analysis
- metric learning
- euclidean space
- machine learning
- data mining