Proceedings of the 2013 International Conference on Advanced Information Engineering and Education Science (ICAIEES 2013)

Improved Partial Least Squares Regression Recommendation Algorithm

Authors
Chunhua Liao, Jianqiang Du, Guohua Jin, Chunlei Chen
Corresponding Author
Chunhua Liao
Available Online December 2013.
DOI
https://doi.org/10.2991/icaiees-13.2013.26How to use a DOI?
Keywords
partial least squares (PLS), kernel algorithm, algorithms improvement, recursive exponentially weighted algorithms
Abstract
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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2013 International Conference on Advanced Information Engineering and Education Science (ICAIEES 2013)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2013
ISBN
978-90-78677-94-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/icaiees-13.2013.26How to use a DOI?
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  -