Response oriented covariates selection (ROCS) for fast block order- and scale-independent variable selection in multi-block scenarios

Mishra, Puneet; Metz, Maxime; Marini, Federico; Biancolillo, Alessandra; Rutledge, Douglas

doi:10.1016/j.chemolab.2022.104551

Cited by 5 publications

(10 citation statements)

References 24 publications

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“…In the case of a single two‐way data block, the algorithm will provide exactly the same solution as the standard covariates selection method 17 (just faster as we take advantage of the computationally more efficient steps in the fCovSel algorithm 27 ). For problems including multiple two‐way type data blocks, that is, a multiblock dataset without any predefined block order, the computationally efficient SKCovSel algorithm will provide exactly the same solution as the ROCS 25 . If the user sets the predefined order of blocks from which features need to be selected, the algorithm will provide a computationally efficient solution consistent with the sequential orthogonalised covariates selection (SO‐CovSel) 19 .…”

Section: Comments On the Skcovsel Algorithmmentioning

confidence: 99%

“…For problems including multiple two-way type data blocks, that is, a multiblock dataset without any predefined block order, the computationally efficient SKCovSel algorithm will provide exactly the same solution as the ROCS. 25 If the user sets the predefined order of blocks from which features need to be selected, the algorithm will provide a computationally efficient solution consistent with the sequential orthogonalised covariates selection (SO-CovSel). 19 Furthermore, when the data set is multiway the algorithm will efficiently provide a solution of the N-CovSel 26 problem computationally consistent with the other CovSel-versions.…”

Section: Comments On the Skcovsel Algorithmmentioning

confidence: 99%

“…Furthermore, the re‐orthogonalisation step speeding up both the PLS and the ROSA have also been used to obtain the fast covariates selection (fCovSel), 27 (by avoiding deflations of the predictor data matrix). In combination, these methods (the PLS, the ROSA and the fCovSel) together with the response oriented covariates selection (ROCS) 25 and SKPLS pave the way for a unified covariates selection algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…When considered as a GS process, the CovSel (like PLS) can be extended to handle multi-block 23 and multiway problems. 24 It should be noted that extensions of the CovSel idea to multiblock 19,25 and multiway 26 data scenarios are already addressed in the literature.…”

mentioning

confidence: 99%

“…In the covariate selection methods, [17][18][19][25][26][27] the variable selection is a stepwise process much like the selection of latent variables (the scores) in PLS. Hence, the CovSel version for multiblock data allows for selecting a variable from any block in each step.…”

mentioning

confidence: 99%

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Swiss knife covariates selection: A unified algorithm for covariates selection in single block, multiblock, multiway, multiway multiblock cases including multiple responses

Mishra

Liland

Indahl

2022

Journal of Chemometrics

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A novel unified covariates selection algorithm called Swiss knife covariates selection (SKCovSel) is presented. It is suitable for selecting covariates in a wide range of data scenarios such as a single two‐way data block, two‐way multiblock, multiway, multiway multiblock, selection of covariates along different modes for multiway data blocks and for selecting covariates for all mentioned cases in multiple response scenarios. In the multiblock case, the method can be scale and data block order‐independent depending on the preference of the user. For multiway scenarios, the method can be multiway mode order independent, depending on the preference of the user. The proposed SKCovSel algorithm generalises the recent speed improvements from faster CovSel to all mentioned data block cases. It also reformulates the multiway case to do proper deflation and rank one slab selections. Particularly, for modelling of multiblock data sets, the SKCovSel follows the “winner takes all” strategy of the stepwise response‐oriented sequential alternation modelling. In the case of multiway data, the SKCovSel strategy considers multiway loading weights after decomposition of a high‐dimensional squared covariance matrix to select features across different modes. The algorithmic steps of the methods are presented, and cases of modelling different data types such as single block, multiblock, multiway multiblock, modes selection for multiway data and multiple responses modelling are shown. The method incorporates all popular covariates selection algorithms existing in the chemometric literature.

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Section: Comments On the Skcovsel Algorithmmentioning

confidence: 99%

Section: Comments On the Skcovsel Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Swiss knife covariates selection: A unified algorithm for covariates selection in single block, multiblock, multiway, multiway multiblock cases including multiple responses

Mishra

Liland

Indahl

2022

Journal of Chemometrics

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

Mishra

Liland

2022

Journal of Chemometrics

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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 covariance estimation.The method allows selecting robust features, and models based on such features in general perform better in terms of prediction accuracy (lower error) than selecting features using equal sample weights for all samples. The algorithm description and tests of the method in single and multiple response scenarios are presented. Method performance is also demonstrated on a real spectral data set.

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Capturing connectivity information from process flow diagrams by sequential-orthogonalized PLS to improve soft-sensor performance

Zhu,

Facco,

Zhao

et al. 2024

Chemometrics and Intelligent Laboratory Systems

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Response oriented covariates selection (ROCS) for fast block order- and scale-independent variable selection in multi-block scenarios

Cited by 5 publications

References 24 publications

Swiss knife covariates selection: A unified algorithm for covariates selection in single block, multiblock, multiway, multiway multiblock cases including multiple responses

Swiss knife covariates selection: A unified algorithm for covariates selection in single block, multiblock, multiway, multiway multiblock cases including multiple responses

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

Capturing connectivity information from process flow diagrams by sequential-orthogonalized PLS to improve soft-sensor performance

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