Name
Abstract | ||
|---|---|---|
When training automated systems, it has been shown to be beneficial to adapt the representation of data by learning a problem-specific metric. This metric is global. We extend this idea and, for the widely used family of k nearest neighbors algorithms, develop a method that allows learning locally adaptive metrics. These local metrics not only improve performance, but are naturally interpretable. To demonstrate important aspects of how our approach works, we conduct a number of experiments on synthetic data sets, and we show its usefulness on real-world benchmark data sets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.neucom.2021.05.105 | Neurocomputing |
Keywords | DocType | Volume |
Interpretable machine learning,Metric learning,Nearest neighbors | Journal | 470 |
ISSN | Citations | PageRank |
0925-2312 | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan Philip Göpfert | 1 | 4 | 1.78 |
| Heiko Wersing | 2 | 1 | 0.35 |
| Barbara Hammer | 3 | 1 | 0.35 |