Soft Dimension Reduction for ICA by Joint Diagonalization on the Stiefel Manifold.
Fabian J. TheisThomas P. CasonPierre-Antoine AbsilPublished in: ICA (2009)
Keyphrases
- dimension reduction
- low dimensional
- manifold learning
- principal component analysis
- euclidean space
- independent component analysis
- feature extraction
- manifold embedding
- blind source separation
- blind separation
- high dimensional
- principle component analysis
- nonlinear manifold
- feature space
- generative topographic mapping
- high dimensional data
- dimensionality reduction
- manifold learning algorithm
- high dimensional problems
- intrinsic dimension
- face recognition
- lower dimensional
- random projections
- linear discriminant analysis
- riemannian manifolds
- preprocessing
- feature selection
- data points
- singular value decomposition
- manifold structure
- diffusion maps
- unsupervised learning
- cluster analysis
- geodesic distance
- image processing
- high dimensionality
- locally linear embedding
- sparse metric learning
- independent components
- linear subspace
- discriminant analysis
- signal processing
- knowledge discovery
- neural network