A Semi-supervised Classification Using Gated Linear Model

Abstract

Semi-supervised learning aims to construct a classifier by making use of both labeled data and unlabeled data. This paper proposes a semi-supervised classification method using a gated linear model, based on the idea of effectively utilizing manifold information. A gating mechanism is firstly trained in a semi-supervised manner to capture manifold information which guides the generation of gate signals. Then the gated linear model is formulated into a linear regression form with the gate signals included. Secondly, a Laplacian regularized least squares (LapRLS) formulation is applied to optimize the linear regression form of the gated linear model. In this way, the gate signals are integrated into the kernel function, which is defined as inner product of the regression vectors. Moreover, this kernel function is used as a better similarity function for graph construction. As a result, the manifold information is ingeniously incorporated into both kernel and graph Laplacian in the LapRLS. Experimental results exhibit the effectiveness of our proposed method.