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