Kernel continuum regression Academic Article uri icon


MeSH Major

  • Acids
  • Bacterial Proteins
  • Enzyme Activation
  • Gene Expression Regulation, Bacterial
  • Mycobacterium tuberculosis
  • N-Acetylmuramoyl-L-alanine Amidase
  • Peptide Hydrolases
  • Stress, Physiological


  • The continuum regression technique provides an appealing regression framework connecting ordinary least squares, partial least squares and principal component regression in one family. It offers some insight on the underlying regression model for a given application. Moreover, it helps to provide deep understanding of various regression techniques. Despite the useful framework, however, the current development on continuum regression is only for linear regression. In many applications, nonlinear regression is necessary. The extension of continuum regression from linear models to nonlinear models using kernel learning is considered. The proposed kernel continuum regression technique is quite general and can handle very flexible regression model estimation. An efficient algorithm is developed for fast implementation. Numerical examples have demonstrated the usefulness of the proposed technique.

publication date

  • August 5, 2013



  • Academic Article



  • eng

PubMed Central ID

  • PMC3777709

Digital Object Identifier (DOI)

  • 10.1016/j.csda.2013.06.016

PubMed ID

  • 24058224

Additional Document Info

start page

  • 190

end page

  • 201


  • 68