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Effective Improved Graph Transduction

机译：有效的改进图形转导

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In this paper, we focus on the problem of shape retrieval and clustering. We put two questions together because they are based on the same method, called Improved Graph Transduction. For shape retrieval, we regard the shape as a node in a graph and the similarity of shapes is represented by the edge of the graph. Then we learn a new distance measure between the query shape and the testing shapes. The main contribution of our work is to merge the most likely node with the query node during the learning process. The appending process helps us to mine the latent information in the propagation. The experimental results on the MPEG-7 data set show that comparing with the existing methods, our method can complete shape retrieval with similar correct rate in less time；For clustering task，the existing literatures in this domain often use the distance measure between the testing data point individual which is proved not enough in the real applications. In this paper, we think about the core concept in semi-supervised learning method, and use a graph to reflect the original distance measure, and combine the density information of the data distribution with the distance measure. Given a set of testing data, we select the original data randomly and use graph transduction iterative on the defined graph. The given algorithm is rapid and steady comparing with the existing clustering method. The experiments show that the novel algorithm is effective for the clustering task.
机译：在本文中，我们关注于形状检索和聚类的问题。我们将两个问题放在一起是因为它们基于相同的方法，称为改进的图形转换。对于形状检索，我们将形状视为图形中的节点，并且形状的相似性由图形的边缘表示。然后，我们学习查询形状和测试形状之间的新距离度量。我们的工作的主要贡献是在学习过程中将最可能的节点与查询节点合并。附加过程有助于我们挖掘传播中的潜在信息。在MPEG-7数据集上的实验结果表明，与现有方法相比，我们的方法可以在较短的时间内以相似的正确率完成形状检索;对于聚类任​​务，该领域的现有文献经常使用测试之间的距离度量。数据点个人，这在实际应用中被证明是不够的。在本文中，我们考虑了半监督学习方法的核心概念，并使用图形来反映原始距离度量，并将数据分布的密度信息与距离度量相结合。给定一组测试数据，我们随机选择原始数据，并在定义的图形上使用图形转换迭代。与现有的聚类方法相比，该算法快速，稳定。实验表明，该算法对聚类任务有效。

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