Cross-Modal Metric Learning for AUC Optimization
Cross-Modal Metric Learning for AUC Optimization
复制标题
用于 AUC 优化的跨模态度量学习
DOI：
10.1109/tnnls.2017.2769128
复制
发表时间：
2018-10-01
影响因子：
10.4
通讯作者：
Yin, Hujun
中科院分区：
文献类型：
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作者：
Huo, Jing;Gao, Yang;Yin, Hujun
跨通道度量学习(CML)处理用于跨通道数据匹配的学习距离函数。现有的方法主要集中在最小化样本对上定义的损失。然而，在许多应用中，类内和类间样本对的数量可能高度不平衡，这可能导致性能恶化或不令人满意。接收机工作特性曲线下面积(AUC)对于不平衡分布问题是一个更有意义的性能指标。为了解决这一问题，并使来自不同模式的样本可以直接进行比较，提出了一种直接最大化AUC的CML方法。该方法可以进一步扩展到集中优化部分AUC(PAUC)，部分AUC是两个特定假阳性率(FPR)之间的AUC。这在某些应用中特别有用，在某些应用中，只有在预定义的假阳性范围内评估的性能是关键的。该方法被描述为对数行列式正则化半定优化问题。为了有效地进行优化，提出了一种小批量邻近点算法。实验验证了该算法在每次迭代时形成小批量的采样对的大小是稳定的。在评估中使用了几个数据集，包括关于不同场景下人脸识别的三个跨模式数据集，以及一个单模式数据集，即标记为Face in the Wild的人脸。实验结果表明，该方法是有效的，与已有方法相比有明显的改进。具体地说，事实证明，pAUC优化的CML在等级1和fpr=0.1%的验证率等性能指标上更具竞争力。
Cross-modal metric learning (CML) deals with learning distance functions for cross-modal data matching. The existing methods mostly focus on minimizing a loss defined on sample pairs. However, the numbers of intraclass and interclass sample pairs can be highly imbalanced in many applications, and this can lead to deteriorating or unsatisfactory performances. The area under the receiver operating characteristic curve (AUC) is a more meaningful performance measure for the imbalanced distribution problem. To tackle the problem as well as to make samples from different modalities directly comparable, a CML method is presented by directly maximizing AUC. The method can be further extended to focus on optimizing partial AUC (pAUC), which is the AUC between two specific false positive rates (FPRs). This is particularly useful in certain applications where only the performances assessed within predefined false positive ranges are critical. The proposed method is formulated as a log-determinant regularized semidefinite optimization problem. For efficient optimization, a minibatch proximal point algorithm is developed. The algorithm is experimentally verified stable with the size of sampled pairs that form a minibatch at each iteration. Several data sets have been used in evaluation, including three cross-modal data sets on face recognition under various scenarios and a single modal data set, the Labeled Faces in the Wild. Results demonstrate the effectiveness of the proposed methods and marked improvements over the existing methods. Specifically, pAUC-optimized CML proves to be more competitive for performance measures such as Rank-1 and verification rate at FPR = 0.1%.