title: Joint learning of labels and distance metric.
abstract: 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.
authors: University of Science and Technology of China, Hefei 230027, China.
authors: Bo
authors: B
authors: Liu
authors: Meng
authors: M
authors: Wang
authors: Richang
authors: R
authors: Hong
authors: Zhengjun
authors: Z
authors: Zha
authors: Xian-Sheng
authors: XS
authors: Hua
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*