A General Framework for Dimensionality Reduction for Large Data Sets.
Barbara HammerMichael BiehlKerstin BunteBassam MokbelPublished in: WSOM (2011)
Keyphrases
- dimensionality reduction
- data sets
- principal component analysis
- high dimensional data
- high dimensional
- high dimensionality
- low dimensional
- pattern recognition
- data reduction
- input space
- structure preserving
- feature space
- linear discriminant analysis
- preprocessing step
- data representation
- singular value decomposition
- random projections
- nonlinear dimensionality reduction
- data points
- manifold learning
- dimensionality reduction methods
- pattern recognition and machine learning
- data analysis
- feature extraction
- lower dimensional
- intrinsic dimensionality
- euclidean distance
- feature selection
- principal components analysis
- linear dimensionality reduction
- diffusion maps
- machine learning
- high dimensional data sets
- real world
- principal components
- linear projection
- artificial intelligence
- multidimensional scaling
- search engine
- kernel learning
- active learning
- multi dimensional