Dimensionality Reduction for Wasserstein Barycenter.
Zachary IzzoSandeep SilwalSamson ZhouPublished in: NeurIPS (2021)
Keyphrases
- dimensionality reduction
- high dimensional
- high dimensional data
- principal component analysis
- high dimensionality
- dimensionality reduction methods
- pattern recognition
- pointwise
- low dimensional
- structure preserving
- manifold learning
- pattern recognition and machine learning
- preprocessing step
- euclidean distance
- feature selection
- data points
- input space
- data representation
- lower dimensional
- diffusion maps
- principal components
- metric learning
- feature space
- feature extraction
- random projections
- principal components analysis
- singular value decomposition
- nonlinear dimensionality reduction
- multidimensional scaling
- data sets
- linear discriminant analysis
- dimension reduction
- graph embedding
- search engine
- kernel pca
- pairwise
- unsupervised feature selection
- sparse representation
- linear dimensionality reduction
- linear projection