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Can we beat overfitting?-A closer look at Cloarec's PLS algorithm
Stockholm University, Faculty of Science, Department of Environmental Science and Analytical Chemistry.
Stockholm University, Faculty of Science, Department of Environmental Science and Analytical Chemistry.
Number of Authors: 22018 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 32, no 6, article id e3002Article in journal (Refereed) Published
Abstract [en]

Random noise has been addressed as a cause of overfitting in partial least squares regression. A previous study pinpointed that one of the sources of overfitting resides in the calculation of scores due to the accumulation of noise in the diagonal of the variance-covariance matrix, and a modified partial least squares regression was proposed with the removal of this diagonal prior to the score calculation. Here, a further modification of the NIPALS algorithm is proposed, with the same ability to overcome overfitting due to noise, but algebraically more similar to the original NIPALS. The results indicate that it is possible to get more reliable auto-prediction R-2 with a cross-validation performance close to that of the original NIPALS algorithm.

Place, publisher, year, edition, pages
2018. Vol. 32, no 6, article id e3002
Keywords [en]
algorithm, overfitting, PLS, partial least squares, projection to latent structures
National Category
Earth and Related Environmental Sciences Chemical Sciences
Identifiers
URN: urn:nbn:se:su:diva-158300DOI: 10.1002/cem.3002ISI: 000435792900002OAI: oai:DiVA.org:su-158300DiVA, id: diva2:1235917
Available from: 2018-07-30 Created: 2018-07-30 Last updated: 2018-07-30Bibliographically approved

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Sousa, Pedro F. M.Åberg, K. Magnus
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