Grassmannian diffusion maps based dimension reduction and classification for high-dimensional data.
K. R. M. dos SantosDimitris G. GiovanisMichael D. ShieldsPublished in: CoRR (2020)
Keyphrases
- dimension reduction
- high dimensional data
- diffusion maps
- manifold learning
- dimensionality reduction
- low dimensional
- nonlinear dimensionality reduction
- high dimensionality
- subspace learning
- high dimensional
- intrinsic dimension
- principal component analysis
- feature extraction
- nearest neighbor
- high dimensions
- feature selection
- data points
- linear discriminant analysis
- data sets
- original data
- random projections
- high dimensional data analysis
- similarity search
- feature space
- input space
- dimensionality reduction methods
- sparse representation
- data analysis
- singular value decomposition
- unsupervised learning
- support vector
- pattern recognition
- locally linear embedding
- underlying manifold
- dimensional data
- high dimension
- machine learning
- decision trees
- principal components analysis
- lower dimensional
- image data
- discriminant analysis