Asymptotic properties of the misclassification rates for Euclidean Distance Discriminant rule in high-dimensional data.
Hiroki WatanabeMasashi HyodoTakashi SeoTatjana PavlenkoPublished in: J. Multivar. Anal. (2015)
Keyphrases
- euclidean distance
- high dimensional data
- asymptotic properties
- dimensionality reduction
- data points
- low dimensional
- principal component analysis
- high dimensional spaces
- high dimensional
- locality preserving projections
- euclidean space
- subspace clustering
- feature extraction
- linear discriminant analysis
- distance metric
- high dimensionality
- nearest neighbor
- distance function
- discriminant analysis
- dimension reduction
- fixed point
- data distribution
- similarity search
- manifold learning
- similarity measure
- feature vectors
- distance measure
- input space
- sparse representation
- data sets
- feature selection
- pattern recognition
- data analysis
- nonlinear dimensionality reduction
- low rank
- random projections
- subspace learning
- dimensional data
- feature space
- training set
- hyperplane
- covariance matrix
- input data
- machine learning
- locally linear embedding
- dimensionality reduction methods
- geodesic distance
- metric learning
- knn
- vector space
- missing data