Wavelet multiscale regression from the perspective of data fusion: new conceptual approaches

Wavelet regression is a very promising technique for modern multivariate calibration and calibration transfer. Multiscale analysis of wavelet scales provides a connection between wavelet regression and data fusion. In this paper, current wavelet regression methods are reviewed from the novel perspective of data fusion. Illustrated by analysis of a public domain near-infrared dataset, the advantages and drawbacks of these methods are examined. For wavelet regression, the non-uniformity of the wavelet components, the multiscale nature of the signal, and the prevention of information leakage are crucial issues that will be addressed.