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PKDD
2015
Springer

A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons

4 years 7 months ago
A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons
We consider the problem of clustering a given dataset into k clusters subject to an additional set of constraints on relative distance comparisons between the data items. The additional constraints are meant to reflect side-information that is not expressed in the feature vectors, directly. Relative comparisons can express structures at finer level of detail than must-link (ML) and cannot-link (CL) constraints that are commonly used for semi-supervised clustering. Relative comparisons are particularly useful in settings where giving 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. Given the learned kernel matrix, a clustering can be obtained by any suitable algorithm, such as kernel k-means. We show empirically that kernels found by our ...
Ehsan Amid, Aristides Gionis, Antti Ukkonen
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Where PKDD
Authors Ehsan Amid, Aristides Gionis, Antti Ukkonen
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