1. [1] D. Zabihzadeh, R. Monsefi, and H. S. Yazdi, "Sparse Bayesian similarity learning based on posterior distribution of data," Engineering Applications of Artificial Intelligence, vol. 67, pp. 173-186, 2018. [
DOI:10.1016/j.engappai.2017.09.023]
2. [2] L. Lin, G. Wang, W. Zuo, X. Feng, and L. Zhang, "Cross-domain visual matching via generalized similarity measure and feature learning," IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 6, pp. 1089-1102, 2017. [
DOI:10.1109/TPAMI.2016.2567386] [
PMID]
3. [3] J. Lu, X. Zhou, Y.-P. Tan, Y. Shang, and J. Zhou, "Neighborhood repulsed metric learning for kinship verification," IEEE transactions on pattern analysis and machine intelligence, vol. 36, no. 2, pp. 331-345, 2014. [
DOI:10.1109/TPAMI.2013.134] [
PMID]
4. [4] S. Bak and P. Carr, "One-Shot Metric Learning for Person Re-identification," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2990-2999. [
DOI:10.1109/CVPR.2017.171]
5. [5] N. Jiang, W. Liu, and Y. Wu, "Order determination and sparsity-regularized metric learning adaptive visual tracking," in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012: IEEE, pp. 1956-1963. [
DOI:10.1109/CVPR.2012.6247897]
6. [6] M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid, "Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation," in Computer Vision, 2009 IEEE 12th International Conference on, 2009: IEEE, pp. 309-316. [
DOI:10.1109/ICCV.2009.5459266]
7. [7] G. Chechik, V. Sharma, U. Shalit, and S. Bengio, "Large Scale Online Learning of Image Similarity Through Ranking," J. Mach. Learn. Res., vol. 11, pp. 1109-1135, 2010. [
DOI:10.1007/978-3-642-02172-5_2]
8. [8] X. Hao, S. C. H. Hoi, J. Rong, and Z. Peilin, "Online Multiple Kernel Similarity Learning for Visual Search," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 36, no. 3, pp. 536-549, 2014, doi: 10.1109/TPAMI-.2013.149. [
DOI:10.1109/TPAMI.2013.149] [
PMID]
9. [9] P. Wu, S. C. H. Hoi, P. Zhao, C. Miao, and Z. Y. Liu, "Online Multi-Modal Distance Metric Learning with Application to Image Retrieval," IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 2, pp. 454-467, 2016, doi: 10.1109/TKDE.2015.2477296. [
DOI:10.1109/TKDE.2015.2477296]
10. [10] J. Li, C. Xu, W. Yang, C. Sun, and D. Tao, "Discriminative Multi-View Interactive Image Re-Ranking," IEEE Transactions on Image Processing, 2017. [
DOI:10.1109/TIP.2017.2651379] [
PMID]
11. [11] A. Bellet, A. Habrard, and M. Sebban, "A Survey on Metric Learning for Feature Vectors and Structured Data," Technical report, 2014.
12. [12] B. Frénay and M. Verleysen, "Classification in the presence of label noise: a survey," IEEE transactions on neural networks and learning systems, vol. 25, no. 5, pp. 845-869, 2013. [
DOI:10.1109/TNNLS.2013.2292894] [
PMID]
13. [13] T. Yang, R. Jin, and A. K. Jain, "Learning from noisy side information by generalized maximum entropy model," in Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010: Citeseer, pp. 1199-1206.
14. [14] K. Huang, R. Jin, Z. Xu, and C.-L. Liu, "Robust metric learning by smooth optimiza-tion," arXiv preprint arXiv:1203.3461, 2012.
15. [15] Y. Nesterov, "Smooth minimization of non-smooth functions," Mathematical programm-ing, vol. 103, no. 1, pp. 127-152, 2005. [
DOI:10.1007/s10107-004-0552-5]
16. [16] D. Wang and X. Tan, "Robust Distance Metric Learning in the Presence of Label Noise," in AAAI, 2014, pp. 1321-1327. [
DOI:10.1609/aaai.v28i1.8903]
17. [17] H. Wang, F. Nie, and H. Huang, "Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization," in Proceedings of the 31st International Conference on Machine Learning (ICML-14), T. Jebara and E. P. Xing, Eds., 2014, [Formatter not found: ResolvePDF]: JMLR Workshop and Conference Proceedings, pp. 1836-1844. [Online]. Available: http://jml-r.org/proceedings/papers/v32/wangj14.pdf. [Online]. Available: http://jmlr.org/proceed-ings/papers/v32/wangj14.pdf
18. [18] S. Xiang, F. Nie, and C. Zhang, "Learning a Mahalanobis distance metric for data clustering and classification," Pattern Recogn., vol. 41, no. 12, pp. 3600-3612, 2008, doi: 10.1016/j.patcog.2008.05.018. [
DOI:10.1016/j.patcog.2008.05.018]
19. [19] D. Wang and X. Tan, "Robust Distance Metric Learning via Bayesian Inference," IEEE Transactions on Image Processing, vol. 27, no. 3, pp. 1542-1553, 2018. [
DOI:10.1109/TIP.2017.2782366] [
PMID]
20. [20] D. Zabihzadeh, R. Monsefi, and H. S. Yazdi, "Sparse Bayesian approach for metric learning in latent space," Knowledge-Based Systems, vol. 178, pp. 11-24, 2019. [
DOI:10.1016/j.knosys.2019.04.009]
21. [21] K. Q. Weinberger and L. K. Saul, "Distance Metric Learning for Large Margin Nearest Neighbor Classification," J. Mach. Learn. Res., vol. 10, pp. 207-244, 2009.
22. [22] S. Al-Obaidi, D. Zabihzadeh, A. S. Rasheed, and R. Monsefi, "Robust Metric Learning based on the Rescaled Hinge Loss," arXiv preprint arXiv:1904.11711, 2019. [
DOI:10.1007/s13042-020-01137-z]
23. [23] F. Wang, W. Zuo, L. Zhang, D. Meng, and D. Zhang, "A kernel classification framework for metric learning," IEEE transactions on neural networks and learning systems, vol. 26, no. 9, pp. 1950-1962, 2015. [
DOI:10.1109/TNNLS.2014.2361142] [
PMID]
24. [24] C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM transactions on intelligent systems and technology (TIST), vol. 2, no. 3, p. 27, 2011. [
DOI:10.1145/1961189.1961199]
25. [25] L. v. d. Maaten and G. Hinton, "Visualizing data using t-SNE," Journal of machine learning research, vol. 9, no. Nov, pp. 2579-2605, 2008.