A semisupervised classifier based on piecewise linear regression model using gated linear network

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.