Partial least squares learning regression for backpropagation network

Tzu-Chien Hsiao; C. W. Lin; H. H.K. Chiang

doi:10.1109/IEMBS.2000.897885

Partial least squares learning regression for backpropagation network

Tzu-Chien Hsiao^*, C. W. Lin, H. H.K. Chiang

^*Corresponding author for this work

Department of Computer Science

Research output: Contribution to journal › Conference article › peer-review

3 Scopus citations

Abstract

The relationship between the Partial Least Squares (PLS) regression and the General Delta Rule (GDR) algorithm is investigated in this report. This PLS regression can be adopted as an efficient pre-learning method for Backpropagation (BP) network. The PLS regression based BP network (named as PLSBP network) has better capacity during training phase. Abided by the statistical concept of the PLS regression, the cost function of this network is guaranteed to be an optimal minimum. The logistic map for network simulation is provided as an example in this report.

Original language	English
Pages (from-to)	975-977
Number of pages	3
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	2
DOIs	https://doi.org/10.1109/IEMBS.2000.897885
State	Published - 1 Dec 2000
Event	22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, United States Duration: 23 Jul 2000 → 28 Jul 2000

Keywords

BP network
Optimal minimum
PLS regression

Access to Document

10.1109/IEMBS.2000.897885

Cite this

@article{615f694e8230424e97eae7012ca1b9ee,

title = "Partial least squares learning regression for backpropagation network",

abstract = "The relationship between the Partial Least Squares (PLS) regression and the General Delta Rule (GDR) algorithm is investigated in this report. This PLS regression can be adopted as an efficient pre-learning method for Backpropagation (BP) network. The PLS regression based BP network (named as PLSBP network) has better capacity during training phase. Abided by the statistical concept of the PLS regression, the cost function of this network is guaranteed to be an optimal minimum. The logistic map for network simulation is provided as an example in this report.",

keywords = "BP network, Optimal minimum, PLS regression",

author = "Tzu-Chien Hsiao and Lin, {C. W.} and Chiang, {H. H.K.}",

year = "2000",

month = dec,

day = "1",

doi = "10.1109/IEMBS.2000.897885",

language = "English",

volume = "2",

pages = "975--977",

journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

issn = "0589-1019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; Conference date: 23-07-2000 Through 28-07-2000",

}

TY - JOUR

T1 - Partial least squares learning regression for backpropagation network

AU - Hsiao, Tzu-Chien

AU - Lin, C. W.

AU - Chiang, H. H.K.

PY - 2000/12/1

Y1 - 2000/12/1

N2 - The relationship between the Partial Least Squares (PLS) regression and the General Delta Rule (GDR) algorithm is investigated in this report. This PLS regression can be adopted as an efficient pre-learning method for Backpropagation (BP) network. The PLS regression based BP network (named as PLSBP network) has better capacity during training phase. Abided by the statistical concept of the PLS regression, the cost function of this network is guaranteed to be an optimal minimum. The logistic map for network simulation is provided as an example in this report.

AB - The relationship between the Partial Least Squares (PLS) regression and the General Delta Rule (GDR) algorithm is investigated in this report. This PLS regression can be adopted as an efficient pre-learning method for Backpropagation (BP) network. The PLS regression based BP network (named as PLSBP network) has better capacity during training phase. Abided by the statistical concept of the PLS regression, the cost function of this network is guaranteed to be an optimal minimum. The logistic map for network simulation is provided as an example in this report.

KW - BP network

KW - Optimal minimum

KW - PLS regression

UR - http://www.scopus.com/inward/record.url?scp=0034445276&partnerID=8YFLogxK

U2 - 10.1109/IEMBS.2000.897885

DO - 10.1109/IEMBS.2000.897885

M3 - Conference article

AN - SCOPUS:0034445276

SN - 0589-1019

VL - 2

SP - 975

EP - 977

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

T2 - 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 23 July 2000 through 28 July 2000

ER -

Partial least squares learning regression for backpropagation network

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this