Cluster pca for outliers detection in high-dimensional data.
George StefatosA. Ben HamzaPublished in: SMC (2007)
Keyphrases
- high dimensional data
- data points
- dimensionality reduction
- variable weighting
- low dimensional
- subspace clustering
- high dimensional
- cluster structure
- principal component analysis
- dimension reduction
- linear discriminant analysis
- nearest neighbor
- lower dimensional
- high dimensionality
- high dimensions
- data sets
- missing values
- k means
- original data
- manifold learning
- detection algorithm
- similarity search
- high dimensional data sets
- data analysis
- feature space
- low rank
- high dimensional spaces
- subspace learning
- input space
- clustering algorithm
- feature extraction
- random projections
- sparse representation
- cluster centers
- outlier detection
- principal components analysis
- principal components
- clustering high dimensional data
- covariance matrix
- missing data
- subspace clusters
- locally linear embedding
- clustering method
- feature selection
- nonlinear dimensionality reduction
- high dimensional datasets
- kernel pca
- pattern recognition
- semi supervised