Using principal components to test normality of high-dimensional data.
Rashid MansoorPublished in: Commun. Stat. Simul. Comput. (2017)
Keyphrases
- high dimensional data
- principal components
- dimensionality reduction
- principal component analysis
- low dimensional
- high dimensional
- high dimensionality
- nearest neighbor
- input space
- data points
- high dimensions
- data sets
- lower dimensional
- manifold learning
- dimension reduction
- low rank
- clustering high dimensional data
- subspace clustering
- feature extraction
- original data
- high dimensional datasets
- linear discriminant analysis
- similarity search
- pattern recognition
- feature space
- variable selection
- data analysis
- hyperplane
- high dimensional spaces
- high dimensional data sets
- principal component regression
- singular value decomposition
- euclidean distance
- dimensional data
- principal components analysis
- feature selection
- discriminant analysis
- diffusion maps
- nonlinear dimensionality reduction
- locally linear embedding
- input image
- computer vision
- learning algorithm