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Deep triplet-driven semi-supervised embedding clustering

Abstract : In most real world scenarios, experts dispose of limited background knowledge that they can exploit for guiding the analysis process. In this context, semi-supervised clustering can be employed to leverage such knowledge and enable the discovery of clusters that meet the analysts’ expectations. To this end, we propose a semi-supervised deep embedding clustering algorithm that exploits triplet constraints as background knowledge within the whole learning process. The latter consists in a two-stage approach where, initially, a low-dimensional data embedding is computed and, successively, cluster assignment is refined via the introduction of an auxiliary target distribution. Our algorithm is evaluated on real-world benchmarks in comparison with state-of-the-art unsupervised and semi-supervised clustering methods. Experimental results highlight the quality of the proposed framework as well as the added value of the new learnt data representation.
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Contributor : Dino Ienco <>
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Dino Ienco, Ruggero Pensa. Deep triplet-driven semi-supervised embedding clustering. 22nd International Conference on Discovery Science (DS), Oct 2019, Split, Croatia. pp.220-234, ⟨10.1007/978-3-030-33778-0_18⟩. ⟨hal-02931058⟩

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