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A new approach for variable influence on projection (VIP) in O2PLS models
Umeå University, Faculty of Science and Technology, Department of Chemistry. MKS Instruments AB, Umeå, Sweden. (Computational Life Science Cluster)
Umeå University, Faculty of Science and Technology, Department of Chemistry. MKS Instruments AB, Umeå, Sweden. (Computational Life Science Cluster)
2016 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 160, 110-124 p.Article in journal (Refereed) Published
Abstract [en]

A novel variable influence on projection approach for O2PLS® models, named VIPO2PLS, is presented in this paper. VIPO2PLS is a model-based method for judging the importance of variables. Its cornerstone is the 2-way formalism of the O2PLS models; i.e. the use of both predictive and orthogonal normalized loadings of the two modelled data matrices, and also a new weighting system based on the sum of squares of both data blocks (X, Y). The VIPO2PLS algorithm has been tested in one synthetic data set and two real cases, and the outcomes have been compared to the PLS-VIP, VIPOPLS, and i-PLS methods. The purpose is to achieve a sharper and enhanced model interpretation of O2PLS models by using the new VIPO2PLS method for assessing the importance of both X- and Y- variables.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 160, 110-124 p.
Keyword [en]
Multi-block variable selection, O2PLS, VIP, Variable importance, Model interpretation, Multivariate calibration
National Category
Chemical Sciences Computer and Information Science
Identifiers
URN: urn:nbn:se:umu:diva-128916DOI: 10.1016/j.chemolab.2016.11.005ISI: 000392684100013OAI: oai:DiVA.org:umu-128916DiVA: diva2:1057799
Available from: 2016-12-19 Created: 2016-12-19 Last updated: 2017-02-27Bibliographically approved
In thesis
1. Novel variable influence on projection (VIP) methods in OPLS, O2PLS, and OnPLS models for single- and multi-block variable selection: VIPOPLS, VIPO2PLS, and MB-VIOP methods
Open this publication in new window or tab >>Novel variable influence on projection (VIP) methods in OPLS, O2PLS, and OnPLS models for single- and multi-block variable selection: VIPOPLS, VIPO2PLS, and MB-VIOP methods
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. When analyzing a large number of data sets (blocks) simultaneously, the number of variables and underlying connections between them grow very much indeed; at this point, reducing the number of variables keeping high interpretability becomes a much needed strategy.

The main direction of research in this thesis is the development of a variable selection method, based on variable influence on projection (VIP), in order to improve the model interpretability of OnPLS models in multiblock data analysis. This new method is called multiblock variable influence on orthogonal projections (MB-VIOP), and its novelty lies in the fact that it is the first multiblock variable selection method for OnPLS models.

Several milestones needed to be reached in order to successfully create MB-VIOP. The first milestone was the development of a single-block variable selection method able to handle orthogonal latent variables in OPLS models, i.e. VIP for OPLS (denoted as VIPOPLS or OPLS-VIP in Paper I), which proved to increase the interpretability of PLS and OPLS models, and afterwards, was successfully extended to multivariate time series analysis (MTSA) aiming at process control (Paper II). The second milestone was to develop the first multiblock VIP approach for enhancement of O2PLS® models, i.e. VIPO2PLS for two-block multivariate data analysis (Paper III). And finally, the third milestone and main goal of this thesis, the development of the MB-VIOP algorithm for the improvement of OnPLS model interpretability when analyzing a large number of data sets simultaneously (Paper IV).

The results of this thesis, and their enclosed papers, showed that VIPOPLS, VIPO2PLS, and MB-VIOP methods successfully assess the most relevant variables for model interpretation in PLS, OPLS, O2PLS, and OnPLS models. In addition, predictability, robustness, dimensionality reduction, and other variable selection purposes, can be potentially improved/achieved by using these methods.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2017. 103 p.
Keyword
Variable influence on projection, VIP, MB-VIOP, orthogonal projections to latent structures, OPLS, O2PLS, OnPLS, variable selection, variable importance in multiblock regression
National Category
Chemical Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-130579 (URN)978-91-7601-620-6 (ISBN)
Public defence
2017-02-15, KB.E3.01, KBC-huset, Umeå campus, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2017-01-25 Created: 2017-01-24 Last updated: 2017-01-24Bibliographically approved

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