Reconstruction of reflectance spectra using robust nonnegative matrix factorization

A. Ben Hamza, David J. Brady

Research output: Contribution to journalArticlepeer-review

66 Scopus citations


In this correspondence, we present a robust statistics-based nonnegative matrix factorization (RNMF) approach to recover the measurements in reflectance spectroscopy. The proposed algorithm is based on the minimization of a robust cost function and yields two equations updated alternatively. Unlike other linear representations, such as principal component analysis, the RNMF technique is resistant to outliers and generates nonnegative-basis functions, which balance the logical attractiveness of measurement functions against their physical feasibility. Experimental results on a spectral library of reflectance spectra are presented to illustrate the much improved performance of the RNMF approach.

Original languageEnglish (US)
Pages (from-to)3637-3642
Number of pages6
JournalIEEE Transactions on Signal Processing
Issue number9
StatePublished - Sep 2006
Externally publishedYes


  • Nonnegative matrix factorization
  • Reflectance spectra
  • Robust statistics

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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