Solving Interpretable Kernel Dimension Reduction.
Chieh WuJared MillerYale ChangMario SznaierJennifer G. DyPublished in: CoRR (2019)
Keyphrases
- dimension reduction
- feature space
- high dimensional problems
- principal component analysis
- variable selection
- feature extraction
- principle component analysis
- low dimensional
- high dimensional
- high dimensionality
- feature selection
- unsupervised learning
- high dimensional data
- manifold learning
- cluster analysis
- preprocessing
- partial least squares
- discriminative information
- kernel function
- dimension reduction methods
- singular value decomposition
- dimensionality reduction
- random projections
- linear discriminant analysis
- kernel methods
- data mining and machine learning
- sparse metric learning
- neural network
- image processing
- kernel pca
- kernel matrix
- active learning
- pattern recognition
- kernel trick
- manifold embedding
- computer vision