An RKHS-based approach to double-penalized regression in high-dimensional partially linear models.
Wenquan CuiHaoyang ChengJiajing SunPublished in: J. Multivar. Anal. (2018)
Keyphrases
- linear models
- variable selection
- high dimensional
- reproducing kernel hilbert space
- additive models
- linear regression
- dimension reduction
- kernel methods
- least squares
- loss function
- model selection
- cross validation
- linear model
- kernel function
- high dimensionality
- low dimensional
- gaussian processes
- input space
- high dimensional data
- dimensionality reduction
- nearest neighbor
- data points
- support vector
- machine learning
- kernel matrix
- nonlinear models