Iterative Reweighted Quantile Regression Using Augmented Lagrangian Optimization for Baseline Correction

Based on baseline is a smooth curve and under the collected spectrum, a robust penalized quantile regression with B-spline basis has been proposed to baseline estimation. Then an iterative reweighted method has been adopted for quantile regression optimization. Instead of man tuning the hyperparameter in penalized quantile regression, augmented Lagrangian method is applied to hyperparameter optimization. Experiments on simulated and real data sets show that our method is more effective in baseline correction than other baseline estimation methods in simulated data set. For real data set, the calibration results after the baseline correction step are better than other preprocessing and baseline correction methods.