Interpretable Approximation of High-Dimensional Data.
Daniel PottsMichael SchmischkePublished in: SIAM J. Math. Data Sci. (2021)
Keyphrases
- high dimensional data
- subspace clustering
- high dimensional
- dimensionality reduction
- low dimensional
- nearest neighbor
- data sets
- high dimensionality
- high dimensions
- similarity search
- input space
- data analysis
- dimension reduction
- missing values
- clustering high dimensional data
- manifold learning
- original data
- data distribution
- data points
- linear discriminant analysis
- lower dimensional
- input data
- high dimensional data sets
- high dimensional datasets
- sparse representation
- complex data
- real world
- high dimensional spaces
- dimensional data
- low rank
- locally linear embedding
- subspace learning
- pattern recognition
- nonlinear dimensionality reduction
- pointwise