Non-linear ICA by Using Isometric Dimensionality Reduction.
John Aldo LeeChristian JuttenMichel VerleysenPublished in: ICA (2004)
Keyphrases
- dimensionality reduction
- manifold learning
- independent component analysis
- principal component analysis
- low dimensional
- neighborhood preserving
- feature extraction
- kernel trick
- high dimensional
- high dimensional data
- input space
- pattern recognition
- data representation
- linear discriminant analysis
- feature space
- dimensionality reduction methods
- blind source separation
- high dimensionality
- face recognition
- data points
- linear dimensionality reduction
- independent components
- lower dimensional
- random projections
- euclidean space
- pattern recognition and machine learning
- preprocessing step
- structure preserving
- subspace learning
- preprocessing
- signal processing
- nonlinear dimensionality reduction
- singular value decomposition
- intrinsic dimensionality
- machine learning
- feature selection
- locally linear embedding
- euclidean distance
- dimension reduction
- principal components
- linear projection
- principle component analysis
- source separation
- statistically independent
- natural image statistics
- nearest neighbor
- supervised dimensionality reduction
- independent components analysis
- neural network