On Principal Component Regression in a High-Dimensional Error-in-Variables Setting.
Anish AgarwalDevavrat ShahDennis ShenPublished in: CoRR (2020)
Keyphrases
- high dimensional
- principal component regression
- variable selection
- kernel space
- principal components
- partial least squares
- dimensionality reduction
- low dimensional
- error rate
- input variables
- high dimensionality
- dimension reduction
- high dimensional data
- data points
- feature space
- parameter space
- data sets
- generalization error
- cross validation
- ls svm
- neural network