Learning Mahalanobis Distance Metric: Considering Instance Disturbance Helps

Learning Mahalanobis Distance Metric: Considering Instance Disturbance Helps

Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3315-3321. https://doi.org/10.24963/ijcai.2017/463

Mahalanobis distance metric takes feature weights and correlation into account in the distance computation, which can improve the performance of many similarity/dissimilarity based methods, such as kNN. Most existing distance metric learning methods obtain metric based on the raw features and side information but neglect the reliability of them. Noises or disturbances on instances will make changes on their relationships, so as to affect the learned metric.In this paper, we claim that considering disturbance of instances may help the distance metric learning approach get a robust metric, and propose the Distance metRIc learning Facilitated by disTurbances (DRIFT) approach. In DRIFT, the noise or the disturbance of each instance is learned. Therefore, the distance between each pair of (noisy) instances can be better estimated, which facilitates side information utilization and metric learning.Experiments on prediction and visualization clearly indicate the effectiveness of the proposed approach.
Keywords:
Machine Learning: Feature Selection/Construction
Machine Learning: Machine Learning