Dimensionality Reduction and Visualization by Doubly Kernelized Unit Ball Embedding.
Behrouz Haji SoleimaniStan MatwinPublished in: Canadian Conference on AI (2018)
Keyphrases
- dimensionality reduction
- multidimensional scaling
- nonlinear dimensionality reduction
- structure preserving
- kernel trick
- graph embedding
- low dimensional
- embedding space
- kernel pca
- principal component analysis
- neighborhood preserving
- locality preserving projections
- data representation
- high dimensional data
- high dimensional
- pattern recognition
- laplacian eigenmaps
- low dimensional spaces
- high dimensionality
- manifold learning
- input space
- multi dimensional scaling
- dimensionality reduction methods
- feature selection
- high dimensional feature space
- data analysis
- learning vector quantization
- euclidean distance
- neural network
- random projections
- feature extraction
- principal components analysis
- cluster analysis
- vector space
- data structure
- feature space
- pattern recognition and machine learning
- locally linear embedding
- interactive visualization
- data sets
- self organizing maps
- kernel function
- visual representation
- information visualization
- visualization tool
- face recognition
- subspace learning
- computer vision
- linear dimensionality reduction
- euclidean space
- fuzzy clustering