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MTC: A Fast and Robust Graph-Based Transductive Learning Method

机译:MTC:一种快速而稳健的基于图的转导学习方法

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Despite the great success of graph-based transductive learning methods, most of them have serious problems in scalability and robustness. In this paper, we propose an efficient and robust graph-based transductive classification method, called minimum tree cut (MTC), which is suitable for large-scale data. Motivated from the sparse representation of graph, we approximate a graph by a spanning tree. Exploiting the simple structure, we develop a linear-time algorithm to label the tree such that the cut size of the tree is minimized. This significantly improves graph-based methods, which typically have a polynomial time complexity. Moreover, we theoretically and empirically show that the performance of MTC is robust to the graph construction, overcoming another big problem of traditional graph-based methods. Extensive experiments on public data sets and applications on web-spam detection and interactive image segmentation demonstrate our method’s advantages in aspect of accuracy, speed, and robustness.
机译:尽管基于图的跨导学习方法取得了巨大的成功,但大多数方法在可伸缩性和鲁棒性方面都存在严重的问题。在本文中,我们提出了一种有效且鲁棒的基于图的转导分类方法,称为最小树切割(MTC),适用于大规模数据。根据图的稀疏表示,我们通过生成树来近似图。利用简单的结构,我们开发了线性时间算法来标记树,以使树的剪切大小最小化。这大大改善了基于图的方法,该方法通常具有多项式时间复杂度。此外,我们从理论和经验上表明,MTC的性能对于图的构建是稳健的,克服了传统基于图的方法的另一个大问题。针对公共数据集的大量实验以及针对Web垃圾邮件检测和交互式图像分割的应用程序证明了我们方法在准确性,速度和鲁棒性方面的优势。

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