Membership-margin based feature selection for mixed type and high-dimensional data: Theory and applications.
Lyamine HedjaziJoseph Aguilar-MartinMarie-Véronique Le LannTatiana Kempowsky-HamonPublished in: Inf. Sci. (2015)
Keyphrases
- high dimensional data
- high dimensionality
- dimensionality reduction
- feature selection
- dimension reduction
- high dimensional
- low dimensional
- nearest neighbor
- data sets
- feature selection algorithms
- similarity search
- data points
- high dimensions
- subspace clustering
- data analysis
- clustering high dimensional data
- multi class
- variable selection
- nonlinear dimensionality reduction
- feature space
- manifold learning
- gene expression data
- lower dimensional
- high dimensional datasets
- missing values
- linear discriminant analysis
- input space
- text categorization
- subspace learning
- high dimensional spaces
- small sample size
- machine learning
- text classification
- input data
- dimensional data
- high dimensional data sets
- unlabeled data
- pattern recognition
- support vector
- computer vision