Quadric Hypersurface Intersection for Manifold Learning in Feature Space.
Fedor PavutnitskiySergei O. IvanovEvgeniy AbramovViacheslav BorovitskiyArtem KlochkovViktor VyalovAnatolii ZaikovskiiAleksandr PetiushkoPublished in: AISTATS (2022)
Keyphrases
- manifold learning
- feature space
- low dimensional
- dimensionality reduction
- high dimensional
- dimension reduction
- nonlinear dimensionality reduction
- feature extraction
- high dimensionality
- diffusion maps
- principal component analysis
- laplacian eigenmaps
- kernel function
- feature selection
- input space
- data points
- high dimensional data
- subspace learning
- head pose estimation
- locally linear embedding
- kernel methods
- input data
- training set
- kernel pca
- high dimensional feature space
- feature mapping
- image representation
- feature vectors
- latent space
- feature set
- embedding space
- low dimensional manifolds
- linear discriminant analysis
- riemannian manifolds
- manifold structure
- lower dimensional
- dimensionality reduction methods
- unsupervised learning
- semi supervised
- image retrieval
- object recognition
- computer vision