Can we beat overfitting?—A closer look at Cloarec's PLS algorithm

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EarlyView Article

  • Published: Mar 7, 2018
  • Author: Pedro F. M. Sousa, K. Magnus Åberg


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 R2 with a cross‐validation performance close to that of the original NIPALS algorithm.

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