Li Ming, Wang Dianhui
IEEE Trans Cybern. 2021 Jan;51(1):359-372. doi: 10.1109/TCYB.2019.2925883. Epub 2020 Dec 22.
Stochastic configuration networks (SCNs) as a class of randomized learner model have been successfully employed in data analytics due to its universal approximation capability and fast modeling property. The technical essence lies in stochastically configuring the hidden nodes (or basis functions) based on a supervisory mechanism rather than data-independent randomization as usually adopted for building randomized neural networks. Given image data modeling tasks, the use of 1-D SCNs potentially demolishes the spatial information of images, and may result in undesirable performance. This paper extends the original SCNs to a 2-D version, called 2DSCNs, for fast building randomized learners with matrix inputs. Some theoretical analysis on the goodness of 2DSCNs against SCNs, including the complexity of the random parameter space and the superiority of generalization, are presented. Empirical results over one regression example, four benchmark handwritten digit classification tasks, two human face recognition datasets, as well as one natural image database, demonstrate that the proposed 2DSCNs perform favorably and show good potential for image data analytics.
随机配置网络（SCNs）作为一类随机学习模型，由于其通用逼近能力和快速建模特性，已成功应用于数据分析。其技术核心在于基于监督机制随机配置隐藏节点（或基函数），而非像构建随机神经网络通常采用的与数据无关的随机化方式。对于图像数据建模任务，使用一维SCNs可能会破坏图像的空间信息，并可能导致不理想的性能。本文将原始的SCNs扩展为二维版本，称为2DSCNs，用于快速构建具有矩阵输入的随机学习器。给出了关于2DSCNs相对于SCNs优势的一些理论分析，包括随机参数空间的复杂性和泛化优越性。在一个回归示例、四个基准手写数字分类任务、两个人脸识别数据集以及一个自然图像数据库上的实证结果表明，所提出的2DSCNs表现良好，在图像数据分析方面具有良好潜力。
