Liu Bo, Wang Meng, Hong Richang, Zha Zhengjun, Hua Xian-Sheng
University of Science and Technology of China, Hefei 230027, China.
IEEE Trans Syst Man Cybern B Cybern. 2010 Jun;40(3):973-8. doi: 10.1109/TSMCB.2009.2034632. Epub 2009 Dec 4.
Machine learning algorithms frequently suffer from the insufficiency of training data and the usage of inappropriate distance metric. In this paper, we propose a joint learning of labels and distance metric (JLLDM) approach, which is able to simultaneously address the two difficulties. In comparison with the existing semi-supervised learning and distance metric learning methods that focus only on label prediction or distance metric construction, the JLLDM algorithm optimizes the labels of unlabeled samples and a Mahalanobis distance metric in a unified scheme. The advantage of JLLDM is multifold: 1) the problem of training data insufficiency can be tackled; 2) a good distance metric can be constructed with only very few training samples; and 3) no radius parameter is needed since the algorithm automatically determines the scale of the metric. Extensive experiments are conducted to compare the JLLDM approach with different semi-supervised learning and distance metric learning methods, and empirical results demonstrate its effectiveness.
机器学习算法经常受到训练数据不足和使用不适当距离度量的困扰。在本文中，我们提出了一种标签与距离度量联合学习（JLLDM）方法，该方法能够同时解决这两个难题。与现有的仅专注于标签预测或距离度量构建的半监督学习和距离度量学习方法相比，JLLDM算法以统一的方案优化未标记样本的标签和马氏距离度量。JLLDM的优势是多方面的：1）可以解决训练数据不足的问题；2）仅用很少的训练样本就能构建出良好的距离度量；3）由于该算法会自动确定度量的尺度，因此无需半径参数。我们进行了大量实验，将JLLDM方法与不同的半监督学习和距离度量学习方法进行比较，实证结果证明了其有效性。
