Evaluation of Non-linearity in MIR Spectroscopic Data for Compressed Learning

Dixon Vimalajeewa, Donagh Berry, Eric Robson, Chamil Kulatunga

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Mid-Infrared (MIR) spectroscopy has emerged as the most economically viable technology to determine milk values as well as to identify a set of animal phenotypes related to health, feeding, well-being and environment. However, Fourier transform-MIR spectra incurs a significant amount of redundant data. This creates critical issues such as increased learning complexity while performing Fog and Cloud based data analytics in smart farming. These issues can be resolved through data compression using unsupervisory techniques like PCA, and perform analytics in the compressed-domain i.e. without decompressing. Compression algorithms should preserve non-linearity of MIRS data (if exists), since emerging advanced learning algorithms can improve their prediction accuracy. This study has investigated the non-linearity between the feature variables in the measurement-domain as well as in two compressed domains using standard Linear PCA and Kernel PCA. Also, the non-linearity between the feature variables and the commonly used target milk quality parameters (Protein, Lactose, Fat) has been analyzed. The study evaluates the prediction accuracy using PLS and LS-SVM respectively as linear and nonlinear predictive models.

Original languageEnglish
Title of host publicationProceeding - 17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
EditorsRaju Gottumukkala, George Karypis, Vijay Raghavan, Xindong Wu, Lucio Miele, Srinivas Aluru, Xia Ning, Guozhu Dong
PublisherIEEE
Pages545-552
Number of pages8
ISBN (Electronic)9781538614808
DOIs
Publication statusPublished - 15 Dec 2017
Event17th IEEE International Conference on Data Mining Workshops, ICDMW 2017 - New Orleans, United States
Duration: 18 Nov 201721 Nov 2017

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2017-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
Country/TerritoryUnited States
CityNew Orleans
Period18/11/201721/11/2017

Keywords

  • Kernel PCA
  • Linear and non-linear regression
  • Mid-Infrared spectroscopy
  • Milk quality prediction
  • Principal Component Analysis

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