Sampling-based dimension reduction for subspace approximation with outliers.
Amit DeshpandeRameshwar PratapPublished in: Theor. Comput. Sci. (2021)
Keyphrases
- dimension reduction
- low dimensional
- principal component analysis
- high dimensional data
- high dimensional
- data points
- dimensionality reduction
- feature space
- linear projection
- feature extraction
- qr decomposition
- linear discriminant analysis
- singular value decomposition
- high dimensional problems
- manifold learning
- partial least squares
- random projections
- unsupervised learning
- feature subspace
- high dimensional data analysis
- monte carlo
- subspace learning
- lower dimensional
- subspace clustering
- discriminative information
- dimension reduction methods
- feature selection
- principal components
- nearest neighbor
- data sets
- preprocessing step
- face recognition
- high dimensionality
- discriminant analysis
- linear subspace
- principal components analysis
- cluster analysis
- sparse representation
- input data
- null space
- optical flow
- scatter matrices
- manifold embedding
- support vector
- nonlinear manifold
- image processing