Webinar
on
SwissKnife
PLS: One tool for modelling single block, multiblock, multiway, multiway
multiblock including multi-responses and meta information
30th
June 2022 at 3pm (UK time)
In the domain of Chemometrics and
multivariate data analysis, partial least squares (PLS) modelling is a widely
used technique. PLS gains its beauty by handling the high collinearity found in
multivariate data by replacing highly covarying variables with common subspaces
spanned by orthogonal latent variables. Furthermore, all can be achieved with
simple steps of linear algebra requiring minimal computation power and time
usage compared to current high-end computing and substantial hyperparameter
tuning required by methods such as deep learning.
PLS can be used for a wide
variety of tasks, for example, single block modelling, multiblock modelling,
multiway data modelling and for task such as regression and classification.
Furthermore, new PLS based approaches can also incorporate meta information to
improve the PLS subspace extraction. However, in the current scenario, there is
a wide range of separate tools and codes available to perform different PLS
tasks. Often when the user needs to perform a new PLS task, they need to start
with a separate mathematical implementation of the PLS techniques.
This study aims to provide a
single solution, i.e., the Swiss knife PLS (SKPLS) modelling approach to enable
a single mathematical implementation to perform analyses of single block,
multiblock, multiway, multiblock multiway, multi-response, and incorporation of
meta information in PLS modelling. It contains all that is needed for any PLS
practitioner to perform both classification and regression tasks. The SKPLS
backbone is the stepwise PLS strategy called response oriented sequential
alternation (ROSA) which we generalize to enable all the mentioned analysis
possibilities. The basic structure of the algorithm is highlighted, and some
example cases of performing single block, multiblock, multiway, multiblock
multiway, multi-response PLS modelling and the incorporation of meta
information in PLS modelling are included.
This webinar will be presented by Puneet Mishra, Wageningen
University.
The webinar will take no longer than one hour.
To register please contact Christine.stevenson@strath.ac.uk