Interpretable Approximation of High-Dimensional Data.
Daniel PottsMichael SchmischkePublished in: CoRR (2021)
Keyphrases
- high dimensional data
- dimensionality reduction
- nearest neighbor
- high dimensional
- low dimensional
- high dimensions
- data sets
- data points
- data analysis
- subspace clustering
- similarity search
- high dimensionality
- input space
- data distribution
- missing values
- original data
- lower dimensional
- high dimensional datasets
- clustering high dimensional data
- dimension reduction
- linear discriminant analysis
- sparse representation
- complex data
- dimensional data
- high dimensional data sets
- variable selection
- text data
- input data
- manifold learning
- low rank
- subspace learning
- nonlinear dimensionality reduction
- computer vision
- learning algorithm
- real world
- neural network