On the orthogonal distance to class subspaces for high-dimensional data classification.
Rui ZhuJing-Hao XuePublished in: Inf. Sci. (2017)
Keyphrases
- high dimensional data
- high dimensionality
- dimension reduction
- low dimensional
- dimensionality reduction
- high dimensional
- nearest neighbor
- high dimensions
- data sets
- high dimensional feature spaces
- small sample size
- data points
- lower dimensional
- data analysis
- subspace clustering
- supervised dimensionality reduction
- intrinsic dimension
- pattern recognition
- input space
- original data
- decision trees
- linear discriminant analysis
- machine learning
- low rank
- class labels
- sparse representation
- feature selection
- distance function
- similarity search
- high dimensional datasets
- intra class
- euclidean distance
- high dimensional spaces
- feature space
- nonlinear dimensionality reduction
- input data
- manifold learning
- high dimensional data sets
- feature extraction
- clustering high dimensional data
- feature vectors
- dimensionality reduction methods
- principal component analysis
- training set
- multi dimensional
- computer vision
- variable weighting