Abstract
A Fourier transform (FT) was used as a tool to reduce the number of variables in pattern recognition of NIR data. Five procedures were designed to select the FT coefficients as the input of the classifier of regularized discriminant analysis (RDA). 11 data sets were analysed and the results were also compared with other dimensionality reduction methods of Principal component analysis (PCA) and univariate feature selection method. Our results demonstrate that FT is a fast and powerful feature reduction method and that its results are comparable to those of PCA as a feature reduction method before classification. It has the additional advantage that feature reduction is applied to individual spectra instead of to a set of spectra, as in the case of PCA.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
