Minimax Sparse Logistic Regression for Very High-Dimensional Feature Selection.
Mingkui TanIvor W. TsangLi WangPublished in: IEEE Trans. Neural Networks Learn. Syst. (2013)
Keyphrases
- high dimensional
- feature selection
- high dimensionality
- dimensionality reduction
- feature space
- microarray data
- gene expression data
- high dimension
- small sample
- dimension reduction
- low dimensional
- text categorization
- sparse data
- small sample size
- variable selection
- feature set
- text classification
- feature selection algorithms
- high dimensional data
- data points
- evaluation function
- feature subset
- classification accuracy
- parameter space
- machine learning
- similarity search
- model selection
- alpha beta
- irrelevant features
- mutual information
- manifold learning
- feature extraction
- support vector
- multi dimensional
- feature ranking
- method for feature selection
- forward selection
- redundant features
- search algorithm
- kernel function
- classification models
- game tree
- discriminative features
- unsupervised learning
- naive bayes
- multi task
- information gain