2022
DOI: 10.1002/cem.3458
|View full text |Cite
|
Sign up to set email alerts
|

Iterative re‐weighted covariates selection for robust feature selection modelling in the presence of outliers (irCovSel)

Abstract: A new method for feature selective modelling in the presence of outliers is presented. The method is a combination of iterative re-weighted partial least squares and the covariates selection approach. The method relies on iterative down-weighting of the outlying samples prior to estimating the squared covariance for covariates selection. In this way, the outlying samples carrying low weights have minimal influence on the squared covariance estimation, while the inliers have the maximum influence on the squared… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

1
20
0

Year Published

2023
2023
2025
2025

Publication Types

Select...
4

Relationship

1
3

Authors

Journals

citations
Cited by 4 publications
(21 citation statements)
references
References 26 publications
1
20
0
Order By: Relevance
“…As can be noted, the key outlying samples (7, 21, 22, 23, 24, 33) were down‐weighted automatically by the iterative weighting approach. Note also that outliers detected were the same outliers as has been detected in earlier studies using the same data 12,18,23 . The capability of the presented algorithm to detect the same outliers as detected in earlier studies confirms the usefulness of the presented algorithm for robust multiblock data modelling.…”
Section: Resultssupporting
confidence: 84%
See 4 more Smart Citations
“…As can be noted, the key outlying samples (7, 21, 22, 23, 24, 33) were down‐weighted automatically by the iterative weighting approach. Note also that outliers detected were the same outliers as has been detected in earlier studies using the same data 12,18,23 . The capability of the presented algorithm to detect the same outliers as detected in earlier studies confirms the usefulness of the presented algorithm for robust multiblock data modelling.…”
Section: Resultssupporting
confidence: 84%
“…Hence, median‐centring was used as it is less affected in the presence of outlying observations. A recently proposed algorithm for robust feature extraction has also used the median‐centring 18 and has already reported its benefit for handling data containing outlying observations.…”
Section: Methodsmentioning
confidence: 99%
See 3 more Smart Citations