Discriminative Graph Embedding for Label Propagation
Discriminative Graph Embedding for Label Propagation
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DOI：
10.1109/tnn.2011.2160873
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发表时间：
2011-09-01
影响因子：
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通讯作者：
Mamitsuka, Hiroshi
中科院分区：
文献类型：
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作者：
Canh Hao Nguyen;Mamitsuka, Hiroshi
在许多应用中，可用的信息被编码在图结构中。这是生物网络、社交网络、网络社区和文档引用中的常见问题。我们研究的问题，分类节点的标签上的相似性图只给一个图结构的节点。传统的机器学习方法通常需要数据驻留在某些欧几里得空间中或具有核表示。将这些方法应用于图上的节点需要将图嵌入到这些空间中。通过嵌入然后学习图上的节点，大多数方法要么是灵活的，不同的学习目标，或足够有效的大规模应用。我们提出了一种方法来嵌入一个图到一个特征空间的歧视性的目的。我们的想法是将标签信息包含到嵌入过程中，使空间表示适合任务。我们设计嵌入目标函数，使以下学习公式成为谱变换。然后，我们将这些谱变换转化为多核学习问题。我们的方法，而被定制的歧视性任务，是有效的，可以扩展到大量的数据集。我们证明了一些模拟的歧视性嵌入的必要性。应用于生物网络问题，我们的方法被证明优于基线。
In many applications, the available information is encoded in graph structures. This is a common problem in biological networks, social networks, web communities and document citations. We investigate the problem of classifying nodes' labels on a similarity graph given only a graph structure on the nodes. Conventional machine learning methods usually require data to reside in some Euclidean spaces or to have a kernel representation. Applying these methods to nodes on graphs would require embedding the graphs into these spaces. By embedding and then learning the nodes on graphs, most methods are either flexible with different learning objectives or efficient enough for large scale applications. We propose a method to embed a graph into a feature space for a discriminative purpose. Our idea is to include label information into the embedding process, making the space representation tailored to the task. We design embedding objective functions that the following learning formulations become spectral transforms. We then reformulate these spectral transforms into multiple kernel learning problems. Our method, while being tailored to the discriminative tasks, is efficient and can scale to massive data sets. We show the need of discriminative embedding on some simulations. Applying to biological network problems, our method is shown to outperform baselines.