Sparse least trimmed squares regression with compositional covariates for high-dimensional data.
Gianna Serafina MontiPeter FilzmoserPublished in: Bioinform. (2021)
Keyphrases
- high dimensional data
- regression model
- high dimensional
- canonical correlation analysis
- regression problems
- sparse representation
- low dimensional
- dimensionality reduction
- high dimensions
- nearest neighbor
- high dimensionality
- data sets
- subspace clustering
- data points
- dimension reduction
- partial least squares
- manifold learning
- input space
- original data
- similarity search
- logistic regression
- lower dimensional
- data analysis
- clustering high dimensional data
- high dimensional feature spaces
- low rank
- response variable
- sample size
- high dimensional data sets
- model selection
- dimensional data
- high dimensional datasets
- input data
- database
- variable selection
- reproducing kernel hilbert space
- high dimensional spaces
- nonlinear dimensionality reduction
- random projections
- gaussian processes
- pattern recognition
- support vector
- sparse coding
- feature extraction
- feature selection
- multi dimensional
- k means
- underlying manifold
- least squares