A Randomized Subspace-based Approach for Dimensionality Reduction and Important Variable Selection.
Di BoHoon HwangboVinit SharmaCorey ArndtStephanie TerMaathPublished in: J. Mach. Learn. Res. (2023)
Keyphrases
- variable selection
- dimensionality reduction
- high dimensional
- high dimensional data
- dimension reduction
- low dimensional
- principal component analysis
- lower dimensional
- high dimensionality
- input variables
- feature space
- linear models
- feature selection
- linear discriminant analysis
- linear projection
- group lasso
- feature extraction
- principal components
- data points
- preprocessing step
- semi supervised dimensionality reduction
- manifold learning
- unsupervised learning
- random projections
- nearest neighbor
- model selection
- dimensionality reduction methods
- high dimensional data analysis
- kernel pca
- cross validation
- neural network
- principal components analysis
- pattern recognition
- low rank
- data sets
- linear combination
- fuzzy logic
- feature vectors
- machine learning