Implementation and Verification of a Robust PLS Regression Algorithm.


Partial least squares regression (PLSR) based methods play an important role when dealing with collinear high dimensional regressors. In this thesis we introduce and im-plement RSIMPLS, a robust alternative to traditional PLSR methods such as non-linear iterative partial least squares (NIPALS) and SIMPLS. The RSIMPLS method has been used to analyse fluid properties using discretized acoustical spectrum data. We find evidence that RSIMPLS performs better than SIMPLS, principal components regression (PCR) and canonical correlation analysis (CCA) for high-dimensional regres-sors (observation DOF p larger than number of observations n). However CCA performs slightly better for low dimensional regressors (n> p). We also find that RSIMPLS con-verges to the same limit as CCA for infinitely large, normally distributed sample sets. Acknowledgements I would like to thank my colleagues and friends at Acosense AB for their support, friend-ship and for all the things we have achieved together. It has truly been a pleasure and a very worthwhile experience. Furthermore I would like to direct a special thanks to Dr. Johan Karlsson at the Fraunhofer-Chalmers Centre for his help and expertise through-out the project and to Prof. Olle Nerman for his insightful comments and helpful advice

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