Neuromorphic tuning of feature spaces to overcome the challenge of low-sample high-dimensional data.
Qinghua ZhouOliver J. SuttonYudong ZhangAlexander N. GorbanValeri A. MakarovIvan Yu. TyukinPublished in: IJCNN (2023)
Keyphrases
- high dimensional data
- high dimensional
- dimensionality reduction
- low dimensional
- high dimensionality
- feature space
- data points
- nearest neighbor
- original data
- lower dimensional
- high dimensions
- clustering high dimensional data
- data sets
- data analysis
- subspace clustering
- manifold learning
- similarity search
- dimension reduction
- low rank
- input space
- input data
- data distribution
- linear discriminant analysis
- high dimensional spaces
- dimensional data
- sparse representation
- training samples
- high dimensional datasets
- feature extraction
- locally linear embedding
- pattern recognition
- nonlinear dimensionality reduction
- small sample size
- high dimensional feature spaces
- nearest neighbor search
- subspace learning
- sample size
- text data
- principal component analysis
- feature vectors
- image retrieval
- feature selection
- learning algorithm
- real world
- neural network