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    Classifying NIR spectra of textile products with kernel methods

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    Authors
    Langeron, Yves
    Doussot, Michel
    Hewson, David
    Duchêne, Jacques
    Affiliation
    Université de technologie de Troyes
    Issue Date
    2007-04-30
    Subjects
    support vector machine
    K-principal component analysis
    kernel alignment
    standard normal variate transformation
    
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    Abstract
    This paper describes the use of kernel methods to classify tissue samples using near-infrared spectra in order to discriminate between samples, either with or without elastane. The aim of this real-world study is to identify an alternative method to classify textile products using near-infrared (NIR) spectroscopy in order to improve quality control, and to aid in the detection of counterfeit garments. The principles behind support vector machines (SVMs), of which the main idea is to linearly separate data, are recalled progressively in order to demonstrate that the decision function obtained is a global optimal solution of a quadratic programming problem. Generally, this solution is found after embedding data in another space F with a higher dimension by the means of a specific non-linear function, the kernel. For a selected kernel, one of the most important and difficult subjects concerning SVM is the determination of tuning parameters. Generally, different combinations of these parameters are tested in order to obtain a machine with adequate classification ability. With the kernel alignment method used in this paper, the most appropriate kernel parameters are identified rapidly. Since in many cases, data are embedded in F, a linear principal component (PC) analysis (PCA) can be considered and studied. The main properties and the algorithm of k-PCA are described here. This paper compares the results obtained in prediction for a linear classifier built in the initial space with the PCs from a PCA and those obtained in F with non-linear PCs from a k-PCA. In the present study, even if there are potentially discriminating wavelengths seen on the NIR spectra, linear discriminant analysis and soft independent modelling of class analogy results show that these wavelengths are not sufficient to build a machine with correct generalisation ability. The use of a non-linear method, such as SVM and its corollary methods, kernel alignment and k-PCA, is then justified.
    Citation
    Langeron Y., Doussot M., Hewson D. J., Duchene J. (2007) 'Classifying NIR spectra of textile products with kernel methods', Engineering Applications of Artificial Intelligence, 20 (3), pp.415-427.
    Publisher
    Elsevier
    Journal
    Engineering Applications of Artificial Intelligence
    URI
    http://hdl.handle.net/10547/623478
    DOI
    10.1016/j.engappai.2006.07.001
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S0952197606001084
    Type
    Article
    Language
    en
    ISSN
    0952-1976
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.engappai.2006.07.001
    Scopus Count
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