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.

主成分分析网络（PCANet）作为一种无监督的浅层网络，在各种体积的数据集上显示出显着的有效性。它采用PCA作为滤波器学习方法进行两层卷积，然后进行块直方图后处理阶段。遵循PCANet的结构，极限学习机自动编码器（ELM-AE）变体被用来替代PCA的角色，它们来自极限学习机网络（ELMNet）和分层式ELMNet。ELMNet在忽略非线性的同时强调了正交投影的重要性。后者引入了复杂的预处理，以克服非线性ELM-AE的缺点。在本文中，我们分析了ELM-AE变体的内在特征，并据此提出了正规化的ELM-AE，它结合了非线性学习能力和近似正交的投影。图像分类实验表明，与无监督卷积神经网络和相关浅层网络相比，无监督特征学习具有更高的有效性。