@inproceedings{805db0d4becb48bbb24667ae2f195c37,
title = "Fast LMNN Algorithm through Random Sampling",
abstract = "The Large Margin Nearest Neighbor (LMNN) metric learning algorithm has been successfully used in many applications and continuously motivates new research works. However, the high computational complexity of training the LMNN algorithm inherent from the k-Nearest Neighbor (kNN) search makes it inapplicable to large datasets, especially when we need to tune the hyper-parameters of the LMNN algorithm. In this paper, we present the fast LMNN algorithm through random sampling. Random sampling method reduces the number of samples that needs to be considered and therefore greatly reduces the computational complexity of training the LMNN algorithm. Our experiments show that when the sample rate is 10\%, the performance of LMNN algorithm is nearly the same to training on all data samples while the training time is only 8\% to 40\% of training on all data samples. We further show that random sampling method can efficiently tune the hyper-parameters of the LMNN algorithm.",
keywords = "LMNN, Metric Learning, Random Sampling",
author = "Kaiyuan Wu and Zhiming Zheng",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 ; Conference date: 14-11-2015 Through 17-11-2015",
year = "2016",
month = jan,
day = "29",
doi = "10.1109/ICDMW.2015.157",
language = "英语",
series = "Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "871--876",
editor = "Xindong Wu and Alexander Tuzhilin and Hui Xiong and Dy, \{Jennifer G.\} and Charu Aggarwal and Zhi-Hua Zhou and Peng Cui",
booktitle = "Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015",
address = "美国",
}