A Winner‐Take‐All Autoencoder Based Pieceswise Linear Model for Nonlinear Regression with Missing Data
A Winner‐Take‐All Autoencoder Based Pieceswise Linear Model for Nonlinear Regression with Missing Data
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DOI：
10.1002/tee.23466
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发表时间：
2021-08
影响因子：
1
通讯作者：
Huilin Zhu;Yanni Ren;Yanling Tian;Jinglu Hu
中科院分区：
文献类型：
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作者：
Huilin Zhu;Yanni Ren;Yanling Tian;Jinglu Hu
缺失数据是预测分析中普遍存在的问题。本文提出了一种基于WTA自动编码器的分段线性模型来解决缺失值场景下的非线性回归问题，该模型由两部分组成：过完备WTA自动编码器和门控线性网络。过完备WTA自动编码器是一种堆叠式去噪自动编码器（SDAE），旨在发挥两个作用：（1）估计缺失值;（2）通过生成一组广泛的二进制门控制序列来实现复杂的划分。此外，提出了一种新的教师信号迭代算法来训练SDAE。另一方面，门控线性网络与生成的二进制门控序列实现了一个灵活的分段线性模型的非线性回归。通过基于门控制序列构成准线性核，然后以与支持向量回归相同的方式识别分段线性模型。实验结果表明，我们提出的混合模型具有更好的性能比传统的模型。© 2021日本电气工程师协会。出版社：Wiley Periodicals LLC
Missing data is a prevailing problem in predictive analytics. In this paper, a winner‐take‐all (WTA) autoencoder‐based piecewise linear model is developed to solve the nonlinear regression problem under the missing value scenario, which consists of two parts: an overcomplete WTA autoencoder and a gated linear network. The overcomplete WTA autoencoder is a stacked denoising autoencoder (SDAE) designed to play two roles: (1) to estimate the missing values; (2) to realize a sophisticated partitioning by generating a broad set of binary gate control sequences. Besides, an iterative algorithm with renewed teacher signals is developed to train the SDAE. On the other hand, the gated linear network with the generated binary gate control sequences implements a flexible piecewise linear model for nonlinear regression. By composing a quasi‐linear kernel based on the gate control sequences, the piecewise linear model is then identified in the same way as a support vector regression. Experimental results have shown that our proposed hybrid model has a better performance than traditional models. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.