Grassmannian Diffusion Maps-Based Dimension Reduction and Classification for High-Dimensional Data.
Ketson R. M. dos SantosDimitrios G. GiovanisMichael D. ShieldsPublished in: SIAM J. Sci. Comput. (2022)
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
- nearest neighbor
- linear discriminant analysis
- high dimensions
- random projections
- feature extraction
- feature selection
- data analysis
- data points
- similarity search
- data sets
- feature space
- original data
- lower dimensional
- input space
- high dimensional data analysis
- pattern recognition
- sparse representation
- input data
- locally linear embedding
- dimensionality reduction methods
- singular value decomposition
- training set
- high dimension
- supervised learning
- manifold structure
- semi supervised
- statistical learning
- dimensional data
- least squares
- discriminant analysis
- support vector
- decision trees