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Swiss Knife feature selection : A novel tool for selecting highly covarying features


Webinar on

Swiss Knife feature selection: 

A novel tool for selecting highly covarying features

12th October 2023 at 3pm (UK time)


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.


Link : https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/full/10.1002/cem.3441


This webinar will be presented by Puneet Mishra, Wageningen Food and Biobased Research


To register please contact Christine.stevenson@strath.ac.uk

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