Inductive Semi-supervised Learning with Graph Convolution Based Regression

Abstract This brief paper introduces a framework for supervised and semi-supervised learning by estimating a non-linear embedding that incorporates Spectral Graph Convolutions structure. The proposed algorithm exploits data-driven graphs in two ways. First, it integrates data smoothness over graphs. Second, its regression loss function jointly uses the data and its graph in the sense that the regressor model sees convolved data samples. The resulting framework can solve the problem of over-fitting on local neighborhood structures for image data having varied natures like outdoor scenes, faces and man-made objects. The proposed Graph Convolution based Semi-supervised Embedding (GCSE) not only provides a new perspective to non-linear embedding research but also induces the standpoint on Spectral Graph Convolutions methods. A series of experiments are conducted on four image datasets in order to compare the proposed method with some state-of-art semi-supervised methods. This evaluation demonstrates the effectiveness of the proposed embedding method.