Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning, as they can exploit the connectivity patterns between labeled and unlabeled data samples to improve learning performance. However, existing graph-based methods either are limited in their ability to jointly model graph structures and data features, such as the classical label propagation methods, or require a considerable amount of labeled data for training and validation due to high model complexity, such as the recent neural-network-based methods. In this paper, we address label efficient semi-supervised learning from a graph filtering perspective. Specifically, we propose a graph filtering framework that injects graph similarity into data features by taking them as signals on the graph and applying a low-pass graph filter to extract useful data representations for classification, where label efficiency can be achieved by conveniently adjusting the strength of the graph filter. Interestingly, this framework unifies two seemingly very different methods -- label propagation and graph convolutional networks. Revisiting them under the graph filtering framework leads to new insights that improve their modeling capabilities and reduce model complexity. Experiments on various semi-supervised classification tasks on four citation networks and one knowledge graph and one semi-supervised regression task for zero-shot image recognition validate our findings and proposals.
翻译:以图表为基础的方法被证明是半监督学习的最有效方法之一,因为它们可以利用贴标签和未贴标签的数据样本之间的连接模式来提高学习绩效,但是,现有的图表方法要么限制了它们联合模拟图形结构和数据特征的能力,例如古典标签传播方法,要么由于模型的复杂性,例如最近的神经网络方法,需要大量标签数据进行培训和验证。本文从图表过滤角度处理标签高效半监督学习的半监督方法,具体地说,我们提议一个图形过滤框架,通过将图示作为信号,将图图图相似性注入数据特征中,并应用低路径图过滤器来提取有用的分类数据表示方式,通过方便地调整图过滤器的强度,可以实现标签效率。有趣的是,这个框架集中了两种看起来非常不同的方法 -- -- 标签传播和图形革命网络。在图形过滤框架内重新研究它们,导致新的洞察力,以提高其建模能力并降低模型复杂性。在各种半监督的图像过滤工作中,对各种半监督的图像过滤任务进行了实验,并验证了我们关于四级的原始分析结果,并试验了各种半监督的系统,并验证了我们关于四级分析结果的半分析。