Data Mining - Partial least squares (PLS)


Partial least squares (PLS) is is a dimension reduction method and uses the same method than principle components regression but it selects the new predictors (principal component) in a supervised way.

The PLS approach attempts to find directions (ie principal component) that help explain both:

  • the response
  • and the original predictors.

PLS look for a direction in which the original predictors varies that are also related to the response.


  • The first partial least squares direction z1, is proportional to the correlation between the response y and the data matrix x.
  • Subsequent directions are found by taking residuals and then repeating the above prescription.


In principle, partial least squares should be a huge gain over principle components regression because it chooses the direction looking at the response but in practice, PLS often does not give a huge gain over principle components regression (PCR).

Ridge and principle components regression work as well and are both simpler.

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