Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey.
Benyamin GhojoghAli GhodsiFakhri KarrayMark CrowleyPublished in: CoRR (2021)
Keyphrases
- dimension reduction
- low dimensional
- high dimensional
- partial least squares
- nonlinear dimensionality reduction
- embedding space
- manifold embedding
- vector space
- graph embedding
- multidimensional scaling
- high dimensional data
- manifold learning
- dimensionality reduction
- latent space
- low dimensional spaces
- high dimensional problems
- variable selection
- principal component analysis
- random projections
- data points
- euclidean space
- high dimensionality
- lower dimensional
- nearest neighbor
- similarity search
- subspace learning
- low dimensional manifolds
- feature space
- model selection
- high dimensions
- input space
- manifold structure
- high dimensional spaces
- feature extraction
- locally linear embedding
- generative topographic mapping
- hamming space
- linear subspace
- neural network
- high dimensional feature space
- semi supervised
- pattern recognition
- similarity measure
- data sets