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Variable influence on projection (VIP) for OPLS models and its applicability in multivariate time series analysis
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC) ; Industrial Doctoral School IDS))
MKS Umetrics AB, Umeå, Sweden.
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
2015 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 146, 297-304 p.Article in journal (Refereed) Published
Abstract [en]

Abstract Recently a new parameter to infer variable importance in orthogonal projections to latent structures (OPLS) was presented. Called OPLS-VIP (variable influence on projection), this parameter is here applied in multivariate time series analysis to achieve an improved diagnosis of process dynamics. To this end, OPLS-VIP has been tested in three real-world industrial data sets; the first data set corresponds to a pulp manufacturing process using a continuous digester, the second one involves data from an industrial heater that experienced problems, and the third data set contains measures of the chemical oxygen demand into the effluent of a newsprint mill. The outcomes obtained using OPLS-VIP are benchmarked against classical PLS-VIP results. It is demonstrated how OPLS-VIP provides a better diagnosis and understanding of the time series behavior than PLS-VIP.

Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 146, 297-304 p.
Keyword [en]
VIP, Variable influence on projection, Multivariate time series analysis, OPLS, Variable selection, Process monitoring
National Category
Chemical Sciences
Identifiers
URN: urn:nbn:se:umu:diva-106759DOI: 10.1016/j.chemolab.2015.05.001ISI: 000360595100031OAI: oai:DiVA.org:umu-106759DiVA: diva2:844653
Available from: 2015-08-07 Created: 2015-08-07 Last updated: 2017-12-04Bibliographically 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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Galindo-Prieto, BeatrizTrygg, Johan

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