ManiFeSt: Manifold-based Feature Selection for Small Data Sets.
David CohenTal ShnitzerYuval KlugerRonen TalmonPublished in: CoRR (2022)
Keyphrases
- small data sets
- feature selection
- data sets
- feature space
- high dimension
- text categorization
- feature set
- feature selection algorithms
- manifold learning
- mutual information
- text classification
- high dimensionality
- multi class
- low dimensional
- dimensionality reduction
- feature weighting
- classification accuracy
- information gain
- feature extraction
- selected features
- gene expression data
- support vector
- irrelevant features
- small sample
- redundant features
- machine learning
- manifold structure
- multi task
- unsupervised feature selection
- classification models
- dimension reduction
- microarray data
- unsupervised learning
- model selection
- support vector machine
- knn
- active learning
- feature subset
- geometric structure
- microarray
- riemannian manifolds
- fisher information
- high dimensional
- decision trees
- selecting relevant features