Randomized approximation methods for the efficient compression and analysis of hyperspectral data.

Area of Science:

• Analytical Chemistry
• Computational Biology
• Data Science

Background:

• Hyperspectral imaging, including matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging, generates vast datasets.
• The "curse of dimensionality" fundamentally limits the analysis of these large, information-rich datasets.

Purpose of the Study:

• To propose and evaluate random matrix-based methods for analyzing large hyperspectral imaging datasets.
• To demonstrate significant data reduction and enable conventional multivariate analysis techniques.

Main Methods:

• Construction of a randomized orthonormal basis for hyperspectral data.
• Application of dimensionality reduction techniques.
• Utilizing principal component analysis (PCA) and clustering on compressed data.

Main Results:

• Achieved data size and dimensionality reductions exceeding 100 times.
• Compression is reversible to within noise limits.
• PCA on compressed MALDI mass spectrometry data yielded nearly identical results to original data analysis but reduced runtime from over an hour to 8 seconds.

Conclusions:

• Random matrix methods offer an effective solution for analyzing large hyperspectral datasets.
• The technique is broadly applicable to various imaging modalities, including optical and Raman spectroscopy.
• Freely available software and data facilitate wider evaluation and adoption of these methods.