IEEE Trans Image Process. 2017 Oct;26(10):4937-4950. doi: 10.1109/TIP.2017.2725578. Epub 2017 Jul 11.
Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs). The new formulation is easy to implement and efficient in training with the off-the-shelf SVM solvers. Two novel metric learning models, namely positive-semidefinite constrained metric learning (PCML) and nonnegative-coefficient constrained metric learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experiments are conducted on general classification, face verification, and person re-identification to evaluate our methods. Compared with the state-of-the-art approaches, our methods can achieve comparable classification accuracy and are efficient in training.
距离度量学习旨在从给定的训练数据中学习有效的距离度量，以便更有效地评估数据样本之间的相似性，从而进行分类。度量学习通常被表述为凸或非凸优化问题，而大多数现有方法基于定制的优化器，对于大规模问题效率不高。在本文中，我们将度量学习表述为具有正半定约束的核分类问题，并通过支持向量机（SVM）的迭代训练来解决。新的公式易于实现，并且使用现成的 SVM 求解器在训练方面非常高效。我们开发了两种新的度量学习模型，即正定约束度量学习（PCML）和非负系数约束度量学习（NCML）。PCML 和 NCML 都可以保证其解的全局最优性。我们在一般分类、人脸验证和人员重识别等任务上进行了实验，以评估我们的方法。与最先进的方法相比，我们的方法可以实现可比的分类准确性，并且在训练方面非常高效。
