Missing values are ubiquitous in the nonlinear regression research, and may lead to bias and a loss of efficiency. Even in a large dataset, values drop-out can substantially reduce the available information for analysis. In this paper, we propose an improved hybrid model to solve the nonlinear regression problem under missing data scenarios, consisting of two parts: an overcomplete winner-take-all (WTA) autoencoder and a multilayer gated linear network. The WTA autoencoder is trained in an adversarial training process by taking advantage of gradually renewed teacher signals and the discrimination of missing values and observed values, and is designed to play two roles: (1) to impute missing components conditioned on observed samples; (2) to generate gate control sequences. On the other hand, the multilayer gated linear network with the generated gate control sequences implements a powerful piecewise linear regression model, whose parameters are optimized by formulating a support vector regression (SVR) with a deep quasi-linear kernel. Experimental results based on different real-world datasets demonstrate the effectiveness of our proposed hybrid model. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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使用深度准线性核的具有缺失值的非线性回归的改进混合模型

缺失值在非线性回归研究中普遍存在，可能导致偏差和效率损失。即使在大型数据集中，值丢失也会大大减少可用于分析的信息。在本文中，我们提出了一种改进的混合模型来解决缺失数据场景下的非线性回归问题，该模型由两部分组成：一个过完全的赢家通吃（WTA）自动编码器和一个多层门控线性网络。WTA 自动编码器在对抗性训练过程中通过利用逐渐更新的教师信号以及对缺失值和观察值的区分进行训练，旨在发挥两个作用：（1）以观察样本为条件估算缺失分量；(2) 生成门控序列。另一方面，具有生成的门控制序列的多层门控线性网络实现了强大的分段线性回归模型，其参数通过制定具有深度准线性核的支持向量回归 (SVR) 进行优化。基于不同真实世界数据集的实验结果证明了我们提出的混合模型的有效性。© 2022 日本电气工程师学会。由 Wiley Periodicals LLC 出版。