An Extreme Learning Machine Architecture Based on Volterra Filtering and PCA

Li CHEN; Ling YANG; Juan DU; Chao SUN; Shenglei DU; Haipeng XI

doi:10.1587/transinf.2017EDP7089

An Extreme Learning Machine Architecture Based on Volterra Filtering and PCA

Li CHEN, Ling YANG, Juan DU, Chao SUN, Shenglei DU, Haipeng XI

Full Text Views

0

Cite this

Summary :

Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. However, it has a linear output layer which may limit the capability of exploring the available information, since higher-order statistics of the signals are not taken into account. To address this, we propose a novel ELM architecture in which the linear output layer is replaced by a Volterra filter structure. Additionally, the principal component analysis (PCA) technique is used to reduce the number of effective signals transmitted to the output layer. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. Then we carry out performance evaluation and application analysis for the proposed architecture in the context of supervised classification and unsupervised equalization respectively, and the obtained results either on publicly available datasets or various channels, when compared to those produced by already proposed ELM versions and a state-of-the-art algorithm: support vector machine (SVM), highlight the adequacy and the advantages of the proposed architecture and characterize it as a promising tool to deal with signal processing tasks.

Publication: IEICE TRANSACTIONS on Information Vol.E100-D No.11 pp.2690-2701

Publication Date: 2017/11/01

Publicized: 2017/08/02

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2017EDP7089

Type of Manuscript: PAPER

Category: Information Network

Authors

Li CHEN
  Lanzhou University
Ling YANG
  Lanzhou University
Juan DU
  Lanzhou University
Chao SUN
  Lanzhou University
Shenglei DU
  Lanzhou University
Haipeng XI
  Lanzhou University

Keyword

extreme learning machines, Volterra filter, principal component analysis, unsupervised equalization, support vector machine

Cite this

Copy

Li CHEN, Ling YANG, Juan DU, Chao SUN, Shenglei DU, Haipeng XI, "An Extreme Learning Machine Architecture Based on Volterra Filtering and PCA" in IEICE TRANSACTIONS on Information, vol. E100-D, no. 11, pp. 2690-2701, November 2017, doi: 10.1587/transinf.2017EDP7089.
Abstract: Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. However, it has a linear output layer which may limit the capability of exploring the available information, since higher-order statistics of the signals are not taken into account. To address this, we propose a novel ELM architecture in which the linear output layer is replaced by a Volterra filter structure. Additionally, the principal component analysis (PCA) technique is used to reduce the number of effective signals transmitted to the output layer. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. Then we carry out performance evaluation and application analysis for the proposed architecture in the context of supervised classification and unsupervised equalization respectively, and the obtained results either on publicly available datasets or various channels, when compared to those produced by already proposed ELM versions and a state-of-the-art algorithm: support vector machine (SVM), highlight the adequacy and the advantages of the proposed architecture and characterize it as a promising tool to deal with signal processing tasks.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7089/_p

Copy

@ARTICLE{e100-d_11_2690,
author={Li CHEN, Ling YANG, Juan DU, Chao SUN, Shenglei DU, Haipeng XI, },
journal={IEICE TRANSACTIONS on Information},
title={An Extreme Learning Machine Architecture Based on Volterra Filtering and PCA},
year={2017},
volume={E100-D},
number={11},
pages={2690-2701},
abstract={Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. However, it has a linear output layer which may limit the capability of exploring the available information, since higher-order statistics of the signals are not taken into account. To address this, we propose a novel ELM architecture in which the linear output layer is replaced by a Volterra filter structure. Additionally, the principal component analysis (PCA) technique is used to reduce the number of effective signals transmitted to the output layer. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. Then we carry out performance evaluation and application analysis for the proposed architecture in the context of supervised classification and unsupervised equalization respectively, and the obtained results either on publicly available datasets or various channels, when compared to those produced by already proposed ELM versions and a state-of-the-art algorithm: support vector machine (SVM), highlight the adequacy and the advantages of the proposed architecture and characterize it as a promising tool to deal with signal processing tasks.},
keywords={},
doi={10.1587/transinf.2017EDP7089},
ISSN={1745-1361},
month={November},}

Copy

TY - JOUR
TI - An Extreme Learning Machine Architecture Based on Volterra Filtering and PCA
T2 - IEICE TRANSACTIONS on Information
SP - 2690
EP - 2701
AU - Li CHEN
AU - Ling YANG
AU - Juan DU
AU - Chao SUN
AU - Shenglei DU
AU - Haipeng XI
PY - 2017
DO - 10.1587/transinf.2017EDP7089
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2017
AB - Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. However, it has a linear output layer which may limit the capability of exploring the available information, since higher-order statistics of the signals are not taken into account. To address this, we propose a novel ELM architecture in which the linear output layer is replaced by a Volterra filter structure. Additionally, the principal component analysis (PCA) technique is used to reduce the number of effective signals transmitted to the output layer. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. Then we carry out performance evaluation and application analysis for the proposed architecture in the context of supervised classification and unsupervised equalization respectively, and the obtained results either on publicly available datasets or various channels, when compared to those produced by already proposed ELM versions and a state-of-the-art algorithm: support vector machine (SVM), highlight the adequacy and the advantages of the proposed architecture and characterize it as a promising tool to deal with signal processing tasks.
ER -