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Communication Dans Un Congrès Année : 2015

Semi-supervised spectral clustering with automatic propagation of pairwise constraints

Résumé

In our data driven world, clustering is of major importance to help end-users and decision makers understanding information structures. Supervised learning techniques rely on ground truth to perform the classification and are usually subject to overtraining issues. On the other hand, unsupervised clustering techniques study the structure of the data without disposing of any training data. Given the difficulty of the task, unsupervised learning tends to provide inferior results to supervised learning. To boost their performance, a compromise is to use learning only for some of the ambiguous classes. In this context, this paper studies the impact of pairwise constraints to unsupervised Spectral Clustering. We introduce a new generalization of constraint propagation which maximizes partitioning quality while reducing annotation costs. Experiments show the efficiency of the proposed scheme.
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Dates et versions

hal-01229055 , version 1 (16-11-2015)

Identifiants

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Nicolas Voiron, Alexandre Benoit, Patrick Lambert, Andrei Filip, Bogdan Ionescu. Semi-supervised spectral clustering with automatic propagation of pairwise constraints. International Workshop on Content-Based Multimedia Indexing (CBMI), Jun 2015, Prague, Czech Republic. pp.1-6, ⟨10.1109/CBMI.2015.7153608⟩. ⟨hal-01229055⟩

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