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R-ELMNet: Regularized extreme learning machine network
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DOI：10.1016/j.neunet.2020.06.009.png)
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Abstract
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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. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Shallow network
PCANet
ELM auto-encoder
ELMNet
R-ELMNet
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