Name
Abstract | ||
|---|---|---|
We consider unsupervised partitioning problems based explicitly or implicitly on the minimization of Euclidean distortions, such as clustering, image or video segmentation, and other change-point detection problems. We emphasize on cases with specific structure, which include many practical situations ranging from mean-based change-point detection to image segmentation problems. We aim at learning a Mahalanobis metric for these unsupervised problems, leading to feature weighting and/or selection. This is done in a supervised way by assuming the availability of several (partially) labeled datasets that share the same metric. We cast the metric learning problem as a large-margin structured prediction problem, with proper definition of regularizers and losses, leading to a convex optimization problem which can be solved efficiently. Our experiments show how learning the metric can significantly improve performance on bioinformatics, video or image segmentation problems. |
Year | Venue | Field |
|---|---|---|
2014 | ICML | Scale-space segmentation,Semi-supervised learning,Pattern recognition,Computer science,Structured prediction,Segmentation-based object categorization,Mahalanobis distance,Image segmentation,Unsupervised learning,Artificial intelligence,Cluster analysis,Machine learning |
DocType | Citations | PageRank |
Conference | 10 | 0.67 |
References | Authors | |
30 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rémi Lajugie | 1 | 101 | 4.68 |
| Francis Bach | 2 | 11490 | 622.29 |
| Sylvain Arlot | 3 | 65 | 6.87 |