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Title: R-ELMNet: regularized extreme learning machine network
Authors: Zhang, Guanghao
Li, Yue
Cui, Dongshun
Mao, Shangbo
Huang, Guang-Bin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Zhang, G., Li, Y., Cui, D., Mao, S. & Huang, G. (2020). R-ELMNet: regularized extreme learning machine network. Neural Networks, 130, 49-59.
Journal: Neural Networks
Abstract: Principal component analysis network (PCANet), as an unsupervised shallow network, demonstrates noticeable effectiveness on datasets of various volumes. It carries a two-layer convolution with PCA as filter learning method, followed by a block-wise histogram post-processing stage. Following the structure of PCANet, extreme learning machine auto-encoder (ELM-AE) variants are employed to replace the PCA's role, which come from extreme learning machine network (ELMNet) and hierarchical ELMNet. ELMNet emphasizes the importance of orthogonal projection while overlooking non-linearity. The latter introduces complex pre-processing to overcome drawback of non-linear ELM-AE. In this paper, we analyze intrinsic characteristics of ELM-AE variants and accordingly propose a regularized ELM-AE, which combines non-linearity learning capability and approximately orthogonal projection. Experiments on image classification show the effectiveness compared to supervised convolutional neural networks and related shallow networks on unsupervised feature learning.
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2020.06.009
Schools: School of Electrical and Electronic Engineering 
Interdisciplinary Graduate School (IGS) 
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: © 2020 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles
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