Feasibility Based Large Margin Nearest Neighbor Metric Learning
Abstract
Large margin nearest neighbor (LMNN) is a metric learner which optimizes the performance of the popular NN classifier. However, its resulting metric relies on pre-selected target neighbors. In this paper, we address the feasibility of LMNN's optimization constraints regarding these target points, and introduce a mathematical measure to evaluate the size of the feasible region of the optimization problem. We enhance the optimization framework of LMNN by a weighting scheme which prefers data triplets which yield a larger feasible region. This increases the chances to obtain a good metric as the solution of LMNN's problem. We evaluate the performance of the resulting feasibility-based LMNN algorithm using synthetic and real datasets. The empirical results show an improved accuracy for different types of datasets in comparison to regular LMNN.
Cite
@article{arxiv.1610.05710,
title = {Feasibility Based Large Margin Nearest Neighbor Metric Learning},
author = {Babak Hosseini and Barbara Hammer},
journal= {arXiv preprint arXiv:1610.05710},
year = {2018}
}
Comments
This is the preprint of the conference paper published in ESANN2018