Partial least squares and random sample consensus in outlier detection

A novel outlier detection method in partial least squares based on random sample consensus is proposed. The proposed algorithm repeatedly generates partial least squares solutions estimated from random samples and then tests each solution for the support from the complete dataset for consistency. A comparative study of the proposed method and leave-one-out cross validation in outlier detection on simulated data and near-infrared data of pharmaceutical tablets is presented. In addition, a comparison between the proposed method and PLS, RSIMPLS, PRM is provided. The obtained results demonstrate that the proposed method is highly efficient.

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► A novel outlier detection method in partial least squares based on random sample consensus is proposed.
► The proposed algorithm repeatedly generates PLS solutions estimated from random samples and then tests each solution for the support based on PLS residuals.
► It models inlier error as an unbiased Gaussian distribution and outlier error as a uniform distribution.
► Compared with the classic PLS and robust methods such as RSIMPLS and PRM, the proposed PLSSAC method shows comparable performance.