Metric Embedding for Nearest Neighbor Classification
| dc.creator | Sriperumbudur, Bharath K. | |
| dc.creator | Lanckriet, Gert R. G. | |
| dc.date | 2007-06-24 | |
| dc.date.accessioned | 2026-07-07T08:12:07Z | |
| dc.date.available | 2026-07-07T08:12:07Z | |
| dc.description | The distance metric plays an important role in nearest neighbor (NN) classification. Usually the Euclidean distance metric is assumed or a Mahalanobis distance metric is optimized to improve the NN performance. In this paper, we study the problem of embedding arbitrary metric spaces into a Euclidean space with the goal to improve the accuracy of the NN classifier. We propose a solution by appealing to the framework of regularization in a reproducing kernel Hilbert space and prove a representer-like theorem for NN classification. The embedding function is then determined by solving a semidefinite program which has an interesting connection to the soft-margin linear binary support vector machine classifier. Although the main focus of this paper is to present a general, theoretical framework for metric embedding in a NN setting, we demonstrate the performance of the proposed method on some benchmark datasets and show that it performs better than the Mahalanobis metric learning algorithm in terms of leave-one-out and generalization errors. | |
| dc.description | 9 pages, 1 table | |
| dc.identifier | https://arxiv.org/abs/0706.3499 | |
| dc.identifier | http://arxiv.org/abs/0706.3499 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/132344 | |
| dc.subject | Machine Learning | |
| dc.title | Metric Embedding for Nearest Neighbor Classification | |
| dc.type | text |