Dependence maps, a dimensionality reduction with dependence distance for high-dimensional data.
Kichun LeeAlexander G. GrayHeeyoung KimPublished in: Data Min. Knowl. Discov. (2013)
Keyphrases
- high dimensional data
- dimensionality reduction
- low dimensional
- high dimensional
- high dimensionality
- nearest neighbor
- high dimensions
- euclidean distance
- manifold learning
- subspace clustering
- data points
- principal component analysis
- similarity search
- linear discriminant analysis
- input space
- dimension reduction
- original data
- nonlinear dimensionality reduction
- intrinsic dimension
- lower dimensional
- subspace learning
- high dimensional spaces
- data sets
- clustering high dimensional data
- feature space
- pattern recognition
- high dimensional datasets
- sparse representation
- feature selection
- data analysis
- dimensional data
- feature extraction
- metric learning
- high dimensional data sets
- random projections
- dimensionality reduction methods
- locally linear embedding
- neural network
- intrinsic dimensionality
- training data
- distance measure
- input data
- distance function