Metric Learning: A Support Vector Approach
Abstract
In this paper, we address the metric learning problem utilizing a margin-based approach. Our metric learning problem is formulated as a quadratic semi-definite programming problem (QSDP) with local neighborhood constraints, which is based on the Support Vector Machine (SVM) framework. The local neighborhood constraints ensure that examples of the same class are separated from examples of different classes by a margin. In addition to providing an efficient algorithm to solve the metric learning problem, extensive experiments on various data sets show that our algorithm is able to produce a new distance metric to improve the performance of the classical K-nearest neighbor (KNN) algorithm on the classification task. Our performance is always competitive and often significantly better than other state-of-the-art metric learning algorithms.
Cite
Text
Nguyen and Guo. "Metric Learning: A Support Vector Approach." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008. doi:10.1007/978-3-540-87481-2_9Markdown
[Nguyen and Guo. "Metric Learning: A Support Vector Approach." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008.](https://mlanthology.org/ecmlpkdd/2008/nguyen2008ecmlpkdd-metric/) doi:10.1007/978-3-540-87481-2_9BibTeX
@inproceedings{nguyen2008ecmlpkdd-metric,
title = {{Metric Learning: A Support Vector Approach}},
author = {Nguyen, Nam and Guo, Yunsong},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2008},
pages = {125-136},
doi = {10.1007/978-3-540-87481-2_9},
url = {https://mlanthology.org/ecmlpkdd/2008/nguyen2008ecmlpkdd-metric/}
}