An Information-Theoretic Approach to Unsupervised Feature Selection for High-Dimensional Data.
Shao-Lun HuangXiangxiang XuLizhong ZhengPublished in: IEEE J. Sel. Areas Inf. Theory (2020)
Keyphrases
- high dimensional data
- unsupervised feature selection
- dimensionality reduction
- low dimensional
- cluster structure
- feature selection
- high dimensional
- high dimensionality
- data matrix
- original data
- nearest neighbor
- low rank
- data distribution
- gene expression data
- subspace clustering
- data analysis
- data sets
- dimension reduction
- similarity search
- feature space
- sparse representation
- manifold learning
- data points
- data clustering
- principal component analysis
- missing values
- linear discriminant analysis
- input data
- computer vision
- pattern recognition
- knowledge discovery
- least squares
- data mining applications
- unsupervised learning
- training set
- euclidean distance
- real world
- neural network