Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases pp 219-234

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

Conference paper

DOI: 10.1007/978-3-319-23528-8_14

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9284)
Cite this paper as:
Amid E., Gionis A., Ukkonen A. (2015) A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons. In: Appice A., Rodrigues P., Santos Costa V., Soares C., Gama J., Jorge A. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science, vol 9284. Springer, Cham

Abstract

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 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.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Helsinki Institute for Information Technology, and Department of Computer ScienceAalto UniversityEspooFinland
  2. 2.Finnish Institute of Occupational HealthHelsinkiFinland

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