Improved Partial Least Squares Regression Recommendation Algorithm
- Chunhua Liao, Jianqiang Du, Guohua Jin, Chunlei Chen
- Corresponding Author
- Chunhua Liao
Available Online December 2013.
- https://doi.org/10.2991/icaiees-13.2013.26How to use a DOI?
- partial least squares (PLS), kernel algorithm, algorithms improvement, recursive exponentially weighted algorithms
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Chunhua Liao AU - Jianqiang Du AU - Guohua Jin AU - Chunlei Chen PY - 2013/12 DA - 2013/12 TI - Improved Partial Least Squares Regression Recommendation Algorithm BT - 2013 International Conference on Advanced Information Engineering and Education Science (ICAIEES 2013) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/icaiees-13.2013.26 DO - https://doi.org/10.2991/icaiees-13.2013.26 ID - Liao2013/12 ER -