随机权重神经网络在低隐藏层规模下仍可实现高分类精度，且支持高效迭代训练。
Residual Random Neural Networks
- 通过迭代残差训练提升随机权重网络性能
- 隐藏层规模与数据维度同阶时仍保持高准确率
- 适用于对模型安全性有要求的场景
单层前馈随机权重神经网络在文献中反复出现。其优势在于训练简化为求解岭回归问题。传统假设认为此类网络需大量隐藏神经元才能达到良好分类效果。本文表明，当数据维度足够高时，隐藏神经元数量与数据维度同阶即可获得良好分类结果。受此启发，我们提出一种高效的迭代残差训练方法，并将算法扩展至最小二乘核版本的神经网络模型。此外，还设计了一种加密（混淆）方法，可用于保护数据和生成的网络模型。
原文摘要 · Abstract (English)
The single-layer feedforward neural network with random weights is a recurring motif in the neural networks literature. The advantage of these networks is their simplified training, which reduces to solving a ridge-regression problem. A general assumption is that these networks require a large number of hidden neurons relative to the dimensionality of the data samples, in order to achieve good classification accuracy. Contrary to this assumption, here we show that one can obtain good classification results even if the number of hidden neurons has the same order of magnitude as the dimensionality of the data samples, if this dimensionality is reasonably high. Inspired by this result, we also develop an efficient iterative residual training method for such random neural networks, and we extend the algorithm to the least-squares kernel version of the neural network model. Moreover, we also describe an encryption (obfuscation) method which can be used to protect both the data and the resulted network model.
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