Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection.
Yang YangHongjian SunJialei GongYali DuDi YuPublished in: CoRR (2022)
Keyphrases
- dimensionality reduction
- manifold learning
- low dimensional
- high dimensional
- diffusion maps
- lower dimensional
- linear projection
- locality preserving projections
- nonlinear dimensionality reduction
- preprocessing step
- discriminant projection
- intrinsic manifold
- feature space
- graph embedding
- high dimensionality
- locally linear embedding
- high dimensional data
- feature set
- data representation
- subspace learning
- principal component analysis
- image features
- data sets
- feature selection
- feature extraction
- underlying manifold
- manifold structure
- approximation algorithms
- dimensionality reduction methods
- feature vectors
- error bounds
- linear discriminant analysis
- nonlinear manifold
- euclidean space
- principal components
- pattern recognition
- singular value decomposition
- closed form
- projection operator
- vector space