O(k)-Equivariant Dimensionality Reduction on Stiefel Manifolds.

Andrew Lee Harlin Lee Jose A. Perea Nikolas Schonsheck Madeleine Weinstein

Published in: CoRR (2023)

Keyphrases

dimensionality reduction
euclidean space
low dimensional
manifold learning
data points
euclidean distance
high dimensional data
high dimensional
nonlinear dimensionality reduction
low dimensional spaces
feature space
neighborhood preserving
laplacian eigenmaps
manifold structure

higher dimensional
embedding space
diffusion maps
riemannian manifolds
feature extraction
high dimensionality
principal component analysis
locally linear embedding
pattern recognition
data representation
nonlinear manifold
dimensionality reduction methods
vector space
pattern recognition and machine learning

geodesic distance
random projections
metric space
feature selection
shape analysis
rotation invariant
principal components
high dimensional spaces
metric learning
underlying manifold
linear discriminant analysis
cell complexes
arbitrary dimension
multidimensional scaling

computer vision
sparse representation
matrix valued
dimension reduction
input space
kernel pca
latent space
structure preserving
data sets
intrinsic dimensionality
discriminant analysis
lower dimensional
covariance matrix