Comparative study of Discrete Wavelet Transforms and Wavelet Tensor Train decomposition to feature extraction of FTIR data of medicinal plants
This work addresses feature extraction for FTIR data in medicinal plant analysis, but it is incremental as it compares existing methods on a specific dataset.
The study compared Wavelet Tensor Train (WTT) and Discrete Wavelet Transform (DWT) for feature extraction from FTIR spectra of 7 medicinal plant species, finding that both methods significantly improved clustering quality and classification accuracy with logistic regression compared to using original spectra, with WTT offering easier tuning due to having only one parameter (rank).
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.