N$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data.
Makoto YamadaAvishek SahaHua OuyangDawei YinYi ChangPublished in: CoRR (2014)
Keyphrases
- minimum redundancy
- high dimensional data
- feature selection
- dimensionality reduction
- manifold learning
- high dimensionality
- low dimensional
- dimension reduction
- data sets
- high dimensional
- high dimensions
- subspace clustering
- similarity search
- nearest neighbor
- data points
- feature space
- data analysis
- text categorization
- sparse representation
- feature selection algorithms
- dimensional data
- high dimensional spaces
- machine learning
- unsupervised learning
- gene expression data
- missing values
- input data
- input space
- variable selection
- linear discriminant analysis
- subspace learning
- feature set
- clustering high dimensional data
- feature subset
- lower dimensional
- support vector
- feature extraction
- high dimensional data sets
- high dimensional datasets
- computer vision
- locally linear embedding
- gene selection
- decision trees
- support vector machine
- text data
- text classification