A Minimax Framework for Classification with Applications to Images and High Dimensional Data.
Qiang ChengHongbo ZhouJie ChengHuiqing LiPublished in: IEEE Trans. Pattern Anal. Mach. Intell. (2014)
Keyphrases
- high dimensional data
- high dimensionality
- image classification
- low dimensional
- dimensionality reduction
- dimension reduction
- nearest neighbor
- high dimensional
- data sets
- high dimensions
- similarity search
- image retrieval
- input image
- lower dimensional
- pattern recognition
- subspace clustering
- feature vectors
- data points
- machine learning
- high dimensional datasets
- image data
- high dimensional spaces
- similarity measure
- subspace learning
- neural network
- image collections
- manifold learning
- input space
- feature space
- riemannian manifolds
- clustering high dimensional data
- sparse representation
- input data
- decision trees
- training set
- principal component analysis
- support vector
- data analysis
- small sample size
- nonlinear dimensionality reduction
- database
- query processing
- support vector machine
- multi dimensional
- euclidean space
- feature extraction
- image processing
- learning algorithm
- linear discriminant analysis