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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Multiple Graph Label Propagation by Sparse Integration
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Multiple Graph Label Propagation by Sparse Integration

机译：稀疏集成的多图标签传播

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摘要

Graph-based approaches have been most successful in semisupervised learning. In this paper, we focus on label propagation in graph-based semisupervised learning. One essential point of label propagation is that the performance is heavily affected by incorporating underlying manifold of given data into the input graph. The other more important point is that in many recent real-world applications, the same instances are represented by multiple heterogeneous data sources. A key challenge under this setting is to integrate different data representations automatically to achieve better predictive performance. In this paper, we address the issue of obtaining the optimal linear combination of multiple different graphs under the label propagation setting. For this problem, we propose a new formulation with the sparsity (in coefficients of graph combination) property which cannot be rightly achieved by any other existing methods. This unique feature provides two important advantages: 1) the improvement of prediction performance by eliminating irrelevant or noisy graphs and 2) the interpretability of results, i.e., easily identifying informative graphs on classification. We propose efficient optimization algorithms for the proposed approach, by which clear interpretations of the mechanism for sparsity is provided. Through various synthetic and two real-world data sets, we empirically demonstrate the advantages of our proposed approach not only in prediction performance but also in graph selection ability.
机译：基于图的方法在半监督学习中最为成功。在本文中，我们专注于基于图的半监督学习中的标签传播。标签传播的一个基本要点是，通过将给定数据的基础流形集成到输入图中，性能会受到严重影响。另一个更重要的一点是，在许多最新的实际应用程序中，相同的实例由多个异构数据源表示。在这种情况下的主要挑战是自动集成不同的数据表示，以实现更好的预测性能。在本文中，我们解决了在标签传播设置下获得多个不同图形的最佳线性组合的问题。针对此问题，我们提出了一种具有稀疏性（以图形组合的系数表示）的新公式，而这是任何其他现有方法都无法正确实现的。此独特功能具有两个重要优点：1）通过消除不相关或有噪声的图形来提高预测性能； 2）结果的可解释性，即轻松识别分类中的信息图。我们为提出的方法提出了有效的优化算法，通过该算法可以对稀疏性机制进行清晰的解释。通过各种综合数据和两个实际数据集，我们从经验上证明了我们提出的方法的优势，不仅在预测性能上，而且在图形选择能力上。

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