Deep Triplet-Driven Semi-supervised Embedding Clustering

  • Dino IencoEmail author
  • Ruggero G. PensaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11828)


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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.IRSTEA, UMR TETIS, LIRMM, Univiversity of MontpellierMontpellierFrance
  2. 2.Department of Computer ScienceUniversity of TurinTurinItaly

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