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Perspective on the Capacity of the Rashomon Effect in Multivariate Data Analysis.

John H Kalivas1

  • 1Department of Chemistry, Idaho State University, Pocatello, Idaho 83209, USA.

Applied Spectroscopy
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Summary
This summary is machine-generated.

This perspective proposes integrating multivariate data analysis principles, like the Theory of Analytic Chemistry (TAC), into spectroscopic modeling. Incorporating the Rashomon effect enhances data characterization and reliability for prediction and classification tasks.

Keywords:
ChemometricsRashomon effectartificial intelligenceexplicate orderfigures of meritimplicate ordermachine learningmatrix effectmodel interpretationsample similarity

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Data Science

Background:

  • Current spectroscopic data analysis often employs fragmented approaches.
  • Multivariate methods like regression, classification, and PARAFAC are used, but can be limited.
  • Different data views (wavelengths, instruments, PARAFAC orders) offer richer information.

Purpose of the Study:

  • To propose expanding spectroscopic modeling by incorporating multivariate ideologies.
  • To highlight the benefits of the Theory of Analytic Chemistry (TAC) and the Rashomon effect.
  • To advocate for a more holistic approach to spectroscopic data analysis.

Main Methods:

  • Applying multivariate principles from the Theory of Analytic Chemistry (TAC).
  • Integrating the Rashomon effect into data analysis workflows.
  • Examining model selection, figures of merit, and sample similarity assessments.

Main Results:

  • Enhanced data characterization through multiple dimensions and fused instruments.
  • Improved reliability in model prediction, outlier detection, and classification.
  • A move towards more comprehensive data analysis by avoiding conventional fragmentation.

Conclusions:

  • The Theory of Analytic Chemistry (TAC) and the Rashomon effect offer a more complete framework for spectroscopic data analysis.
  • Interpretation of spectral models is cautioned against due to the Rashomon effect.
  • Parallels are drawn between spectroscopic data and fundamental concepts in physics and consciousness.