Coupling randomisation and sparse modelling for the exploratory analysis of large hyperspectral datasets.

Sparse-based models are a powerful tools for data compression, variable reduction, and model complexity reduction. Nevertheless, their major issue is the high computational time needed in large matrices. This manuscript proposes, for the first time, to couple randomised decomposition as a first step before sparsity calculations, followed by a projection of the full data onto a reduced-sparse set of loadings that will drastically reduce the time needed for calculations and built models that are equally reliable as their sparse-based homologous. While this new approach might be valid for several scenarios (exploration, regression and classification), we will focus on exploration methods (like Principal Component Analysis – PCA) applied to large datasets of hyperspectral images. Two datasets of different complexity have been tested, and the benefits of the coupled randomisation and sparse PCA (rsPCA) are extensively studied. • Coupling randomisation and sparsity for the analysis of massive hyperspectral data. • Focused on sparse-PCA, but extrapolable to classification and regression. • Comprehensive study of randomisation effects on the sparse loadings. [ABSTRACT FROM AUTHOR]