指数族图的匹配与排序
Exponential Family Graph Matching and Ranking
摘要：
我们提出了一种学习二分图中最大权重匹配预测的方法。该方法包括在指数族中进行最大后验估计，并具有足够的统计量来编码排列和数据特征。虽然推理是在一般困难，我们表明，一个非常相关的应用程序-网页排名-精确的推理是有效的。对于一般的模型实例，适当的采样器是现成的。与现有的最大利润率匹配模型相反，我们的方法在统计上是一致的，此外，随着样本量的增加，实验表明优于这种模型的改进。我们将该方法应用于计算机视觉中的图匹配以及用于学习网页排名的标准基准数据集，其中我们获得了最先进的结果，特别是改进了最大边缘变量。这种方法相对于最大余量替代品的缺点是它的运行时间为大型图，这是比较高的。
英文摘要：
We present a method for learning max-weight matching predictors in bipartite graphs. The method consists of performing maximum a posteriori estimation in exponential families with sufficient statistics that encode permutations and data features. Although inference is in general hard, we show that for one very relevant application - web page ranking - exact inference is efficient. For general model instances, an appropriate sampler is readily available. Contrary to existing max-margin matching models, our approach is statistically consistent and, in addition, experiments with increasing sample sizes indicate superior improvement over such models. We apply the method to graph matching in computer vision as well as to a standard benchmark dataset for learning web page ranking, in which we obtain state-of-the-art results, in particular improving on max-margin variants. The drawback of this method with respect to max-margin alternatives is its runtime for large graphs, which is comparatively high.
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