An information-theoretic approach to universal feature selection in high-dimensional inference.
Shao-Lun HuangAnuran MakurLizhong ZhengGregory W. WornellPublished in: ISIT (2017)
Keyphrases
- high dimensional
- feature selection
- high dimensionality
- dimensionality reduction
- microarray data
- feature space
- low dimensional
- small sample
- gene expression data
- dimension reduction
- high dimension
- similarity search
- high dimensional data
- text categorization
- variable selection
- text classification
- sparse data
- feature extraction
- bayesian networks
- machine learning
- nearest neighbor
- small sample size
- data points
- mutual information
- discriminative features
- high dimensional problems
- metric space
- support vector
- feature set
- inference process
- irrelevant features
- parameter space
- neural network
- model selection
- feature selection algorithms
- selected features
- kernel function
- feature weighting
- dynamic bayesian networks
- bayesian inference
- multi dimensional
- support vector machine
- knn
- decision trees
- data mining