Abstract
Distance metric learning (DML) has achieved great success in many computer vision tasks. However, most existing DML algorithms are based on point estimation, and thus are sensitive to the choice of training examples and tend to be over-fitting in the presence of label noise. In this paper, we present a robust DML algorithm based on Bayesian inference. In particular, our method is essentially a Bayesian extension to a previous classic DML method-large margin nearest neighbor classification and we use stochastic variational inference to estimate the posterior distribution of the transformation matrix. Furthermore, we theoretically show that the proposed algorithm is robust against label noise in the sense that an arbitrary point with label noise has bounded influence on the learnt model. With some reasonable assumptions, we derive a generalization error bound of this method in the presence of label noise. We also show that the DML hypothesis class in which our model lies is probably approximately correct-learnable and give the sample complexity. The effectiveness of the proposed method1 is demonstrated with state of the art performance on three popular data sets with different types of label noise.1 A MATLAB implementation of this method is made available at http://parnec.nuaa.edu.cn/xtan/Publication.htm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.