Dimensionality reduction: Beyond the Johnson-Lindenstrauss bound.
Yair BartalBen RechtLeonard J. SchulmanPublished in: SODA (2011)
Keyphrases
- dimensionality reduction
- johnson lindenstrauss
- high dimensional
- low dimensional
- high dimensional data
- high dimensionality
- upper bound
- feature extraction
- principal component analysis
- principal components
- manifold learning
- data representation
- data points
- pattern recognition and machine learning
- structure preserving
- feature space
- pattern recognition
- lower dimensional
- linear dimensionality reduction
- lower bound
- input space
- feature selection
- linear projection
- case study
- error bounds
- singular value decomposition
- dimensionality reduction methods
- linear discriminant analysis
- random projections
- preprocessing step
- euclidean distance
- dimension reduction
- kernel learning
- real time
- locally linear embedding
- intrinsic dimensionality
- unsupervised feature selection
- model selection
- nearest neighbor
- semi supervised
- data sets