This paper aims to improve the performance of partial least squares regression, and then, improve efficiency of its implementation. In this paper we provide a novel derivation based on optimization for the partial least squares (PLS) algorithm. The derivation shows that only one of either the X- or the Y- matrix needs to be deflated during the sequential process of computing latent factors. And then, based on this derivation, an improved recursive exponentially weighted PLS regression algorithm was proposed. And the improved algorithm is obviously superior to traditional PLS regression algorithm on performance.
CITATION STYLE
Liao, C., Du, J., Jin, G., & Chen, C. (2013). Improved partial least squares regression recommendation algorithm. In Proceedings of the 2013 International Conference on Advanced Information Engineering and Education Science, ICAIEES 2013 (pp. 91–94). Atlantis Press. https://doi.org/10.2991/icaiees-13.2013.26
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