Dimensionality reduction: theoretical perspective on practical measures.
Yair BartalNova FandinaOfer NeimanPublished in: NeurIPS (2019)
Keyphrases
- dimensionality reduction
- real world
- theoretical underpinnings
- high dimensional data
- feature extraction
- principal component analysis
- low dimensional
- random projections
- practical application
- dimensionality reduction methods
- high dimensional
- pattern recognition and machine learning
- practical significance
- quantitative measures
- high dimensionality
- practical problems
- theoretical considerations
- manifold learning
- principal components
- linear discriminant analysis
- theoretical analysis
- image processing
- artificial intelligence
- data mining