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Variable influence on projection (VIP) for orthogonal projections to latent structures (OPLS)
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
MKS Umetrics, Umeå, Sweden.
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
2014 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 28, no 8, 623-632 p.Article in journal (Refereed) Published
Abstract [en]

A new approach for variable influence on projection (VIP) is described, which takes full advantage of the orthogonal projections to latent structures (OPLS) model formalism for enhanced model interpretability. This means that it will include not only the predictive components in OPLS but also the orthogonal components. Four variants of variable influence on projection (VIP) adapted to OPLS have been developed, tested and compared using three different data sets, one synthetic with known properties and two real-world cases.

Place, publisher, year, edition, pages
2014. Vol. 28, no 8, 623-632 p.
Keyword [en]
chemometrics, variable influence on projection, VIP, OPLS, variable selection, PLS
National Category
Analytical Chemistry
Research subject
Computer Science; Analytical Chemistry; Statistics
Identifiers
URN: urn:nbn:se:umu:diva-90733DOI: 10.1002/cem.2627ISI: 000340504100007OAI: oai:DiVA.org:umu-90733DiVA: diva2:730854
Projects
Innovative Multivariate Model Based Approaches For Industry.
Funder
Swedish Research Council, 2011-604
Note

Additional supporting information may be found in the online version of this article at the publisher’s web site.

Available from: 2014-06-30 Created: 2014-06-30 Last updated: 2017-12-05Bibliographically 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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