Preserving Cluster Features in Imputing High Dimensional Data with Extensive Missing Rate.
Chih LaiCarolin PoschenLisa Maria SteinheuerJörg HackermüllerPublished in: Big Data (2022)
Keyphrases
- high dimensional data
- missing values
- subspace clustering
- high dimensionality
- data points
- dimensionality reduction
- low dimensional
- nearest neighbor
- data sets
- high dimensions
- high dimensional
- dimension reduction
- feature set
- data analysis
- feature extraction
- manifold learning
- clustering high dimensional data
- feature vectors
- missing data
- clustering algorithm
- similarity search
- high dimensional feature spaces
- high dimensional spaces
- text data
- neural network
- original data
- sparse representation
- principal component analysis
- feature space
- input space
- lower dimensional
- real world
- linear discriminant analysis
- particle swarm optimization
- multiple types
- subspace learning
- high dimensional datasets
- cluster structure
- high dimensional data sets
- machine learning
- variable weighting