Name
Abstract | ||
|---|---|---|
We consider the problem of metric learning subject to a set of constraints on relative-distance comparisons between the data items. Such constraints are meant to reflect side-information that is not expressed directly in the feature vectors of the data items. The relative-distance constraints used in this work are particularly effective in expressing structures at finer level of detail than must-link (ML) and cannot-link (CL) constraints, which are most commonly used for semi-supervised clustering. Relative-distance constraints are thus useful in settings where providing an ML or a CL constraint is difficult because the granularity of the true clustering is unknown. Our main contribution is an efficient algorithm for learning a kernel matrix using the log determinant divergence --- a variant of the Bregman divergence --- subject to a set of relative-distance constraints. The learned kernel matrix can then be employed by many different kernel methods in a wide range of applications. In our experimental evaluations, we consider a semi-supervised clustering setting and show empirically that kernels found by our algorithm yield clusterings of higher quality than existing approaches that either use ML/CL constraints or a different means to implement the supervision using relative comparisons. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Learning | Artificial intelligence,Granularity,Cluster analysis,Kernel (linear algebra),Mathematical optimization,Feature vector,Pattern recognition,Level of detail,Bregman divergence,Constrained clustering,Kernel method,Mathematics,Machine learning |
DocType | Volume | Citations |
Journal | abs/1612.00086 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ehsan Amid | 1 | 21 | 6.83 |
| Aristides Gionis | 2 | 6808 | 386.81 |
| antti ukkonen | 3 | 114 | 7.47 |