Semisupervised classification aims to construct a classifier by making use of both labeled data and unlabeled data. This paper proposes a semisupervised classifier based on a piecewise linear regression model implemented by using a gated linear network. The semisupervised classifier is constructed in two steps. In the first step, instead of estimating the break points of a piecewise linear model directly, a label‐guided autoencoder‐based semisupervised gating mechanism is designed to generate binary gate control signals to realize the partitioning. In the second step, the piecewise linear model is first transformed into linear regression form, and the linear parameters are then optimized globally by a Laplacian regularized least squares (LapRLS) algorithm using a kernel function comprising the gate control signals obtained in the first step. Moreover, the composed kernel function is used as a better similarity function for the graph construction. As a result, we capture data manifold from both labeled and unlabeled data, and the data manifold is ingeniously incorporated into both the kernel and the graph Laplacian in LapRLS. Numerical experiments on various real‐world datasets exhibit the effectiveness of the proposed method. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

中文翻译：

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基于门控线性网络的分段线性回归模型的半监督分类器

半监督分类旨在通过同时使用标记数据和未标记数据来构造分类器。本文提出了一种基于分段线性回归模型的半监督分类器，该模型使用门控线性网络实现。半监督分类器分两步构建。在第一步中，不是直接估计分段线性模型的断点，而是设计了一种基于标签引导的基于自动编码器的半监督门控机制，以生成二进制门控制信号以实现分区。在第二步中，首先将分段线性模型转换为线性回归形式，然后使用包含第一步中获得的门控制信号的核函数，通过拉普拉斯正则化最小二乘（LapRLS）算法全局优化线性参数。而且，组合的核函数被用作图构造的更好的相似性函数。结果，我们从标记和未标记的数据中捕获了数据流形，并且数据流形被巧妙地合并到了LapRLS的内核和图拉普拉斯算子中。在各种现实世界数据集上的数值实验证明了该方法的有效性。©2020日本电气工程师学会。由John Wiley＆Sons，Inc.发布 在各种现实世界数据集上的数值实验证明了该方法的有效性。©2020日本电气工程师学会。由John Wiley＆Sons，Inc.发布 在各种现实世界数据集上的数值实验证明了该方法的有效性。©2020日本电气工程师学会。由John Wiley＆Sons，Inc.发布