Bayesian regression based on principal components for high-dimensional data.
Jaeyong LeeHee-Seok OhPublished in: J. Multivar. Anal. (2013)
Keyphrases
- principal components
- high dimensional data
- dimensionality reduction
- principal component analysis
- low dimensional
- high dimensionality
- high dimensional
- high dimensions
- subspace clustering
- dimension reduction
- manifold learning
- nearest neighbor
- lower dimensional
- similarity search
- data points
- input space
- linear discriminant analysis
- data sets
- high dimensional spaces
- original data
- nonlinear dimensionality reduction
- hyperplane
- data analysis
- pattern recognition
- clustering high dimensional data
- feature space
- principal components analysis
- principal component regression
- low rank
- singular value decomposition
- covariance matrix
- high dimensional datasets
- feature extraction
- feature selection
- independent component analysis
- dimensional data
- euclidean distance
- feature set
- high dimensional data sets
- clustering algorithm