Name
Abstract | ||
|---|---|---|
Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. We present mixed-integer optimization approaches to find optimal distance metrics that generalize the Mahalanobis metric extensively studied in the literature. Additionally, we generalize and improve upon leading methods by removing reliance on pre-designated target neighbors, triplets, and similarity pairs. Another salient feature of our method is its ability to enable active learning by recommending precise regions to sample after an optimal metric is computed to improve classification performance. This targeted acquisition can significantly reduce computational burden by ensuring training data completeness, representativeness, and economy. We demonstrate classification and computational performance of the algorithms through several simple and intuitive examples, followed by results on real image and medical datasets. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Data point,Active learning,Representativeness heuristic,Mahalanobis distance,Artificial intelligence,Real image,Cluster analysis,Completeness (statistics),Mathematics,Machine learning,Salient |
DocType | Volume | Citations |
Journal | abs/1803.10647 | 0 |
PageRank | References | Authors |
0.34 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Krishnan Kumaran | 1 | 0 | 0.34 |
| Dimitri Papageorgiou | 2 | 0 | 0.34 |
| Yutong Chang | 3 | 0 | 0.34 |
| Minhan Li | 4 | 7 | 4.60 |
| Martin Takác | 5 | 752 | 49.49 |