9th Sep, 2010

One Last Time on Accuracy of PLS Algorithms

Scott Ramos of Infometrix wrote me last week and noted that he had followed the discussion on the accuracy of PLS algorithms. Given that they had done considerable work comparing their BIDIAG2 implementation with other PLS algorithms, he was “surprised” with my original post on the topic. He was kind enough to send me a MATLAB implementation of the PLS algorithms included in their product Pirouette for inclusion in my comparison.

Below you’ll find a figure that compares regression vectors from NIPALS, SIMPLS, DSPLS and the BIDIAG2 code provided by Scott. The figure was generated for a tall-skinny X-block (our “melter” data) and for a short-fat X-block (NIR of pseudo-gasoline mixture). Note that I used our SIMPLS and DSPLS that include re-orthogonalization, as that is now our default. While I haven’t totally grokked Scott’s code, I do see where it includes an explicit re-orthogonalization of the scores as well.

Note that all the algorithms are quite similar, with the biggest differences being less than one part in 1010. The BIDIAG2 code provided by Scott (hot pink with stars) is the closest to NIPALS for the tall skinny case, while being just a little more different than the other algorithms for the short fat case.

This has been an interesting exercise. I’m always surprised when I find there is still much to learn about something I’ve already been thinking about for 20+ years! It is certainly a good lesson in watching out for numerical accuracy issues, and in how accuracy can be improved with some rather simple modifications.

BMW

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[…] regression algorithm. The reasons for this are evident from several of my recent posts concerning accuracy and speed of various algorithms. If you have done a PLS regression in PLS_Toolbox or Solo, you have […]

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