Feature Selection of High Dimensional Data by Adaptive Potential Particle Swarm Optimization.
Xingyue HuangYizhou ChiYu ZhouPublished in: CEC (2019)
Keyphrases
- high dimensional data
- high dimensionality
- particle swarm optimization
- dimensionality reduction
- feature selection
- dimension reduction
- low dimensional
- high dimensional
- nearest neighbor
- subspace clustering
- data sets
- manifold learning
- data analysis
- similarity search
- high dimensions
- feature extraction
- gene expression data
- clustering high dimensional data
- high dimensional datasets
- linear discriminant analysis
- data points
- high dimensional spaces
- data distribution
- nonlinear dimensionality reduction
- small sample size
- sparse representation
- feature space
- text categorization
- high dimensional data sets
- microarray data
- original data
- support vector
- pattern recognition
- input space
- machine learning
- missing values
- subspace learning
- lower dimensional
- multi objective
- principal component analysis
- feature set
- preprocessing step
- variable selection
- knn
- decision trees
- text classification
- text data
- particle swarm optimizer
- genetic algorithm