Sparse Dimensionality Reduction Revisited.
Mikael Møller HøgsgaardLior KammaKasper Green LarsenJelani NelsonChris SchwiegelshohnPublished in: CoRR (2023)
Keyphrases
- dimensionality reduction
- high dimensional
- random projections
- sparse representation
- low dimensional
- high dimensional data
- high dimensionality
- principal component analysis
- manifold learning
- compressive sensing
- feature selection
- pattern recognition and machine learning
- sparse data
- sparse matrix
- linear discriminant analysis
- subspace learning
- compressed sensing
- structure preserving
- data representation
- singular value decomposition
- data points
- dimensionality reduction methods
- neural network
- nonlinear dimensionality reduction
- pattern recognition
- input space
- feature space
- preprocessing step
- dictionary learning
- canonical correlation analysis
- diffusion maps
- underlying manifold
- data sets
- kernel function
- kernel pca
- metric learning
- intrinsic dimensionality
- graph embedding
- similarity measure
- genetic algorithm