Abstract
Abstract. We present a Bayesian variational inference scheme for semi-
supervised clustering in which data is supplemented with side information
in the form of common labels. There is no mutual exclusion of classes
assumption and samples are represented as a combinatorial mixture over
multiple clusters. We illustrate performance on six datasets and ¯nd a
positive comparison against constrained K-means clustering.
Translated title of the contribution | A Variational Approach to Semi-Supervised Clustering |
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Original language | English |
Title of host publication | ESANN |
Pages | 11 - 16 |
Number of pages | 5 |
Publication status | Published - 2009 |
Bibliographical note
Name and Venue of Event: ESANN2009, Bruges BelgiumConference Proceedings/Title of Journal: Proceedings ESANN2009