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

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Abstract

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.

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Acknowledgements

The authors sincerely acknowledge Anthony J. Myles and Roberto N. Feudale for their helpful comments during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, 19716, USA

    Yang Liu & Steven D. Brown

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  1. Yang Liu
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  2. Steven D. Brown
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Correspondence to Steven D. Brown.

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Liu, Y., Brown, S.D. Wavelet multiscale regression from the perspective of data fusion: new conceptual approaches. Anal Bioanal Chem 380, 445–452 (2004). https://doi.org/10.1007/s00216-004-2776-x

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