Feature Extraction/Selection in High-Dimensional Spectral Data

Development of advanced sensing technology has multiplied the volume of spectral data, which is one of the most common types of data encountered in many research fields that require advanced mathematical methods with highly efficient computation. Examples of the fields in which spectral data abound include nearinfrared, mass spectroscopy, magnetic resonance imaging, and nuclear magnetic resonance spectroscopy. The introduction of a variety of spectroscopic techniques makes it possible to investigate changes in composition in a spectrum and to quantify them without complex preparation of samples. However, a major limitation in the analysis of spectral data lies in the complexity of the signals generated by the presence of a large number of correlated features. Figure 1 displays a high-level diagram of the overall process of modeling and analyzing spectral data. The collected spectra should be first preprocessed to ensure high quality data. Preprocessing steps generally include denoising, baseline correction, alignment, and normalization. Feature extraction/selection identifies the important features for prediction, and relevant models are constructed through the learning processes. The feedback path from the results of the validation step enables control and optimization of all previous steps. Explanatory analysis and visualization can provide initial guidelines that make the subsequent steps more efficient. This chapter focuses on the feature extraction/selection step in the modeling and analysis of spectral data. Particularly, throughout the chapter, the properties of feature extraction/selection procedures are demonstrated with spectral data from high-resolution nuclear magnetic resonance spectroscopy, one of the widely used techniques for studying metabolomics.