This article introduces a scheme for semi-supervised learning by estimating a flexible non-linear data representation that exploits Spectral Graph Convolutions structure. Structured data are exploited in order to determine non-linear and linear models. The introduced scheme takes advantage of data-driven graphs at two levels. First, it incorporates manifold smoothness that is naturally encoded by the graph itself. Second, the regression model is built on the convolved data samples that are derived from the data and their associated graph. The proposed semi-supervised embedding can tackle the problem of over-fitting on neighborhood structures for image data. The proposed Graph Convolution-based Semi-supervised Embedding paves the way to new theoretical and application perspectives related to the non-linear embedding. Indeed, building flexible models that adopt convolved data samples can enhance both the data representation and the final performance of the learning system. Several experiments are conducted on six image datasets for comparing the introduced scheme with some state-of-the-art semi-supervised approaches. This empirical evaluation shows the effectiveness of the proposed embedding scheme.

中文翻译：

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通过基于图卷积的回归的弹性嵌入用于半监督分类

本文通过估计利用谱图卷积结构的灵活非线性数据表示来介绍一种半监督学习方案。利用结构化数据来确定非线性和线性模型。引入的方案在两个级别上利用了数据驱动的图。首先，它结合了由图本身自然编码的流形平滑度。其次，回归模型建立在从数据及其关联图派生的卷积数据样本上。所提出的半监督嵌入可以解决图像数据邻域结构的过度拟合问题。所提出的基于图卷积的半监督嵌入为与非线性嵌入相关的新理论和应用前景铺平了道路。确实，构建采用卷积数据样本的灵活模型可以增强学习系统的数据表示和最终性能。在六个图像数据集上进行了几个实验，以将引入的方案与一些最先进的半监督方法进行比较。该经验评估显示了所提出的嵌入方案的有效性。