False Discovery Rate Control for Grouped Variable Selection in High-Dimensional Linear Models Using the T-Knock Filter.
Jasin MachkourMichael MumaDaniel P. PalomarPublished in: EUSIPCO (2022)
Keyphrases
- variable selection
- linear models
- high dimensional
- false discovery rate
- cross validation
- dimension reduction
- high dimensional data
- feature selection
- low dimensional
- dimensionality reduction
- model selection
- group lasso
- control system
- hypothesis testing
- additive models
- feature space
- sparse representation
- pattern recognition
- machine learning