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2017 IEEE International Conference on Data Mining Workshops (ICDMW) (2017)
New Orleans, Louisiana, USA
Nov. 18, 2017 to Nov. 21, 2017
ISSN: 2375-9259
ISBN: 978-1-5386-3800-2
pp: 545-552
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.
cloud computing, dairy products, dairying, data analysis, data compression, Fourier transform infrared spectroscopy, learning (artificial intelligence), least squares approximations, principal component analysis, support vector machines

D. Vimalajeewa, D. Berry, E. Robson and C. Kulatunga, "Evaluation of Non-linearity in MIR Spectroscopic Data for Compressed Learning," 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, Louisiana, USA, 2018, pp. 545-552.
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