Parsimonious and robust multivariate calibration with rational function Least Absolute Shrinkage and Selection Operator and rational function Elastic Net

Pekka Teppola (Corresponding Author), V.-M. Taavitsainen

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)

Abstract

This paper presents new methods for multivariate calibration. A unique aspect is that this approach uses rational functions with either Least Absolute Shrinkage and Selection Operator (LASSO) or Elastic Net (ENET), and builds parsimonious models in an automated way via cross-validation. Rational function modeling provides robustness, as will be briefly demonstrated. Interestingly, rational function models are also flexible, in that occasionally they are reduced to ordinary linear models based on cross-validation. Thus, model complexity is not forced to take the form of rational functions.

Additional benefits arise from the use of LASSO and ENET. While LASSO uses only ℓ1 norm on regression coefficients, ENET combines the best of both worlds by using ℓ1 and ℓ2 norms. The former (ℓ1) provides variable selection while the latter (ℓ2) handles collinearity via shrinkage of regression coefficients. Rational functions are highly collinear if full rank is used and, thus, not necessarily robust unless either ℓ1 or ℓ2 norm is used on the regression coefficients. The use of ℓ1 norm allows for a more parsimonious model that can potentially be more robust. This is contrary to the use of a broadband spectrum that is likely to be contaminated at some point in the future by unknown spectral interferences. The real benefits seem to originate from the combination of rational functions and ENET. Note that LASSO solutions form a subset of ENET solutions and are thus included in ENET.
Original languageEnglish
Pages (from-to)57-68
Number of pages12
JournalAnalytica Chimica Acta
Volume768
DOIs
Publication statusPublished - 2013
MoE publication typeA1 Journal article-refereed
EventXIII Conference on Chemometrics in Analytical Chemistry - Budapest, Hungary
Duration: 25 Jun 201229 Jun 2012

Fingerprint

Rational functions
Calibration
Mathematical operators
calibration
Linear Models
Set theory
norm
modeling

Keywords

  • chemometrics
  • elastic net
  • LASSO
  • multi-point
  • multivariate calibration
  • NIR
  • rational function
  • RF-ENET

Cite this

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title = "Parsimonious and robust multivariate calibration with rational function Least Absolute Shrinkage and Selection Operator and rational function Elastic Net",
abstract = "This paper presents new methods for multivariate calibration. A unique aspect is that this approach uses rational functions with either Least Absolute Shrinkage and Selection Operator (LASSO) or Elastic Net (ENET), and builds parsimonious models in an automated way via cross-validation. Rational function modeling provides robustness, as will be briefly demonstrated. Interestingly, rational function models are also flexible, in that occasionally they are reduced to ordinary linear models based on cross-validation. Thus, model complexity is not forced to take the form of rational functions.Additional benefits arise from the use of LASSO and ENET. While LASSO uses only ℓ1 norm on regression coefficients, ENET combines the best of both worlds by using ℓ1 and ℓ2 norms. The former (ℓ1) provides variable selection while the latter (ℓ2) handles collinearity via shrinkage of regression coefficients. Rational functions are highly collinear if full rank is used and, thus, not necessarily robust unless either ℓ1 or ℓ2 norm is used on the regression coefficients. The use of ℓ1 norm allows for a more parsimonious model that can potentially be more robust. This is contrary to the use of a broadband spectrum that is likely to be contaminated at some point in the future by unknown spectral interferences. The real benefits seem to originate from the combination of rational functions and ENET. Note that LASSO solutions form a subset of ENET solutions and are thus included in ENET.",
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Parsimonious and robust multivariate calibration with rational function Least Absolute Shrinkage and Selection Operator and rational function Elastic Net. / Teppola, Pekka (Corresponding Author); Taavitsainen, V.-M.

In: Analytica Chimica Acta, Vol. 768, 2013, p. 57-68.

Research output: Contribution to journalArticleScientificpeer-review

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KW - RF-ENET

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