A novel robust PLS regression method inspired from boosting principles: RoBoost-PLSR

Robustness properties of a robust PLS regression method

The presence of multicollinearity in regression data is no exception in real life examples. Instead of applying ordinary regression methods, biased regression techniques such as Principal Component Regression and Ridge Regression have been developed to cope with such data sets. In this paper we consider Partial Least Squares (PLS) regression by means of the SIMPLS algorithm. Because the SIMPLS ...

Robust PLS regression based on simple least median squares regression

A family of regression methods derived from standard PLSR

We present a new regression method derived from standard PLSR which has a geometric point of view and consists of two projections. In the first, scores are obtained after an oblique projection of the spectra onto the loadings. In the second, the vector of response values is projected orthogonally onto the scores. A metric is introduced for the oblique projection and a new algorithm for calculat...

Robust Regression by Boosting the Median

Most boosting regression algorithms use the weighted average of base regressors as their final regressor. In this paper we analyze the choice of the weighted median. We propose a general boosting algorithm based on this approach. We prove boosting-type convergence of the algorithm and give clear conditions for the convergence of the robust training error. The algorithm recovers ADABOOST and ADA...

A Robust Boosting Method for Mislabeled Data

Abstract We propose a new, robust boosting method by using a sigmoidal function as a loss function. In deriving the method, the stagewise additive modelling methodology is blended with the gradient descent algorithms. Based on intensive numerical experiments, we show that the proposed method is actually better than AdaBoost and other regularized method in test error rates in the case of noisy, ...