Evaluation of Non-linearity in MIR Spectroscopic Data for Compressed Learning
| Autor: | Eric Robson, Dixon Vimalajeewa, Chamil Kulatunga, Donagh P. Berry |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science 020206 networking & telecommunications Pattern recognition Cloud computing 02 engineering and technology Walton Institute for Information and Communications Systems Science Kernel principal component analysis Support vector machine ComputingMethodologies_PATTERNRECOGNITION Analytics Kernel (statistics) Principal component analysis 0202 electrical engineering electronic engineering information engineering Data analysis Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence business Telecommunications Software and Systems Group Data compression |
| Zdroj: | ICDM Workshops |
| Popis: | 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 de-compressing. 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 non-linear predictive models. |
| Databáze: | OpenAIRE |
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