Learning Robust Distance Metric with Side Information via Ratio Minimization of Orthogonally Constrained L21-Norm Distances
Learning Robust Distance Metric with Side Information via Ratio Minimization of Orthogonally Constrained L21-Norm Distances
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DOI：
10.24963/ijcai.2019/417
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Kai Liu;Lodewijk Brand;Hua Wang;F. Nie
中科院分区：
文献类型：
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作者：
Kai Liu;Lodewijk Brand;Hua Wang;F. Nie
度量学习的目的是学习给定数据集的距离度量，在测量数据对象之间的距离或相似性方面起着重要作用。由于其广泛的实用性，在过去的几十年里，它吸引了机器学习和相关领域的大量兴趣。本文提出了学习的距离度量的边信息的形式必须链接和不能链接。给定成对约束，我们的目标是学习一个Mahalanobis距离，使必须链接中的数据对与不能链接中的数据对的距离之比最小化。与许多使用传统平方L2范数距离的现有论文不同，我们通过使用非平方L2范数距离开发了一个对数据噪声或离群值不太敏感的鲁棒模型。在我们的目标中，正交约束是强制执行，以避免退化的解决方案。为了解决我们的目标，我们推导出一个有效的迭代求解算法。我们已经进行了大量的实验，这证明了我们的方法比国家的最先进的优越性。
Metric Learning, which aims at learning a distance metric for a given data set, plays an important role in measuring the distance or similarity between data objects. Due to its broad usefulness, it has attracted a lot of interest in machine learning and related areas in the past few decades. This paper proposes to learn the distance metric from the side information in the forms of must-links and cannot-links. Given the pairwise constraints, our goal is to learn a Mahalanobis distance that minimizes the ratio of the distances of the data pairs in the must-links to those in the cannot-links. Different from many existing papers that use the traditional squared L2-norm distance, we develop a robust model that is less sensitive to data noise or outliers by using the not-squared L2-norm distance. In our objective, the orthonormal constraint is enforced to avoid degenerate solutions. To solve our objective, we have derived an efficient iterative solution algorithm. We have conducted extensive experiments, which demonstrated the superiority of our method over state-of-the-art.