Differentially private low-dimensional representation of high-dimensional data.
Yiyun HeThomas StrohmerRoman VershyninYizhe ZhuPublished in: CoRR (2023)
Keyphrases
- high dimensional data
- low dimensional
- differentially private
- high dimensional
- dimensionality reduction
- manifold learning
- high dimensions
- latent space
- data points
- high dimensionality
- dimension reduction
- input space
- nearest neighbor
- lower dimensional
- subspace clustering
- original data
- data sets
- linear discriminant analysis
- similarity search
- principal component analysis
- high dimensional spaces
- feature space
- data analysis
- dimensional data
- differential privacy
- data distribution
- nonlinear dimensionality reduction
- euclidean space
- random projections
- sparse representation
- intrinsic dimension
- clustering high dimensional data
- pattern recognition
- neural network
- euclidean distance
- image data
- underlying manifold