Solving Interpretable Kernel Dimensionality Reduction.
Chieh WuJared MillerYale ChangMario SznaierJennifer G. DyPublished in: NeurIPS (2019)
Keyphrases
- dimensionality reduction
- kernel pca
- kernel learning
- kernel function
- input space
- kernel trick
- feature space
- high dimensional
- high dimensionality
- high dimensional data
- class separability
- low dimensional
- kernel methods
- kernel discriminant analysis
- graph embedding
- data representation
- principal component analysis
- random projections
- feature selection
- kernel matrix
- reproducing kernel hilbert space
- multiple kernel learning
- principal components
- machine learning
- pattern recognition and machine learning
- linear discriminant analysis
- component analysis
- evolutionary algorithm
- data analysis
- support vector
- similarity measure
- feature extraction
- image processing
- learning algorithm