  Fourier-transform infra-red (FTIR) spectra of samples from 7 plant species
were used to explore the influence of preprocessing and feature extraction on
efficiency of machine learning algorithms. Wavelet Tensor Train (WTT) and
Discrete Wavelet Transforms (DWT) were compared as feature extraction
techniques for FTIR data of medicinal plants. Various combinations of signal
processing steps showed different behavior when applied to classification and
clustering tasks. Best results for WTT and DWT found through grid search were
similar, significantly improving quality of clustering as well as
classification accuracy for tuned logistic regression in comparison to original
spectra. Unlike DWT, WTT has only one parameter to be tuned (rank), making it a
more versatile and easier to use as a data processing tool in various signal
processing applications.
