HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections.
Ines ChamiAlbert GuDat NguyenChristopher RéPublished in: ICML (2021)
Keyphrases
- dimensionality reduction
- linear projection
- high dimensional
- high dimensional data
- principal component analysis
- data representation
- pattern recognition and machine learning
- feature extraction
- high dimensionality
- dimensionality reduction methods
- three dimensional
- data points
- radon transform
- random projections
- input space
- principal components
- lower dimensional
- feature space
- tomographic reconstruction
- structure preserving
- manifold learning
- partial differential equations
- discrete tomography
- low dimensional
- optimization criterion
- image reconstruction from projections
- discrete sets
- nonlinear dimensionality reduction
- data sets
- linear discriminant analysis
- feature selection
- coordinate ascent
- conservation laws
- image processing
- image reconstruction
- linear dimensionality reduction
- dimension reduction
- polyhedral objects
- metric learning
- kernel pca
- projection matrix
- multidimensional scaling
- euclidean distance
- kernel learning
- graph embedding
- supervised dimensionality reduction
- locally linear embedding