%0 Journal Article %1 aylin2009comparison %A Alin, Aylin %D 2009 %J Statistical Papers %K algorithms pls %N 4 %P 711--720 %T Comparison of PLS algorithms when number of objects is much larger than number of variables %U http://dx.doi.org/10.1007/s00362-009-0251-7 %V 50 %X Abstract  NIPALS and SIMPLS algorithms are the most commonly used algorithms for partial least squares analysis. When the number of objects, N, is much larger than the number of explanatory, K, and/or response variables, M, the NIPALS algorithm can be time consuming. Even though the SIMPLS is not as time consuming as the NIPALS and can be preferredover the NIPALS, there are kernel algorithms developed especially for the cases where N is much larger than number of variables. In this study, the NIPALS, SIMPLS and some kernel algorithms have been used to builtpartial least squares regression model. Their performances have been compared in terms of the total CPU time spent for thecalculations of latent variables, leave-one-out cross validation and bootstrap methods. According to the numerical results,one of the kernel algorithms suggested by Dayal and MacGregor (J Chemom 11:73–85, 1997) is the fastest algorithm.

@article{aylin2009comparison, abstract = {Abstract  NIPALS and SIMPLS algorithms are the most commonly used algorithms for partial least squares analysis. When the number of objects, N, is much larger than the number of explanatory, K, and/or response variables, M, the NIPALS algorithm can be time consuming. Even though the SIMPLS is not as time consuming as the NIPALS and can be preferredover the NIPALS, there are kernel algorithms developed especially for the cases where N is much larger than number of variables. In this study, the NIPALS, SIMPLS and some kernel algorithms have been used to builtpartial least squares regression model. Their performances have been compared in terms of the total CPU time spent for thecalculations of latent variables, leave-one-out cross validation and bootstrap methods. According to the numerical results,one of the kernel algorithms suggested by Dayal and MacGregor (J Chemom 11:73–85, 1997) is the fastest algorithm.}, added-at = {2010-02-28T04:52:36.000+0100}, author = {Alin, Aylin}, biburl = {https://www.bibsonomy.org/bibtex/2ea249e68606a8f7a7de6abc383a164a9/vivion}, description = {SpringerLink - Zeitschriftenbeitrag}, interhash = {9c51e4abac3b1520e6dfe430c69803e2}, intrahash = {ea249e68606a8f7a7de6abc383a164a9}, journal = {Statistical Papers}, keywords = {algorithms pls}, month = {#aug#}, number = 4, pages = {711--720}, timestamp = {2010-02-28T04:52:36.000+0100}, title = {Comparison of PLS algorithms when number of objects is much larger than number of variables}, url = {http://dx.doi.org/10.1007/s00362-009-0251-7}, volume = 50, year = 2009 }