An Extreme Learning Machine Architecture Based on Volterra Filtering and PCA

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

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.11   pp.2690-2701
Publication Date: 2017/11/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7089
Type of Manuscript: PAPER
Category: Information Network
extreme learning machines,  Volterra filter,  principal component analysis,  unsupervised equalization,  support vector machine,  

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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.