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Fast Methods for Reducing Dimensionality of Spectral Data for Their Visualization

  • V. A. VaginEmail author
  • A. E. Krasnov
  • D. N. Nicol’skii
Article

We have developed fast methods for reducing the dimensionality of spectral data, such as IR spectroscopy data, chromatography data, etc. In contrast to the widely known methods for projection of data with dimensionality N read counts onto spaces of lower dimensionality, having computational complexity of order N × N, proportional to the dimensionality of the covariance matrices for the data, in order to reduce the computational time we propose using new methods that can be realized in a sliding window over n read counts. As a result, the fast methods have computational complexity of order n × N. We present the results of computer experiments on reducing the dimensionality of IR spectra for automotive gasolines. The problem of reducing the dimensionality of IR spectra is important for both their graphical visualization and decreasing the multicollinearity and reducing the influence of noise in simulating behavior or analysis of parameters depending on the spectral characteristics.

Keywords

spectral data infrared spectrum reducing dimensionality visualization 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • V. A. Vagin
    • 1
    Email author
  • A. E. Krasnov
    • 2
  • D. N. Nicol’skii
    • 2
  1. 1.Scientific and Technological Center of Unique Instrument-Making of the Russian Academy of SciencesMoscowRussia
  2. 2.Center of Realization of State Educational Policy and Informational TechnologiesMoscowRussia

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