Adaptive conformal semi-supervised vector quantization for dissimilarity data

Xibin Zhu, Frank-michael Schleif, Barbara Hammer

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
335 Downloads (Pure)


Existing semi-supervised learning algorithms focus on vectorial data given in Euclidean space. But many real life data are non-metric, given as (dis-)similarities which are not widely addressed. We propose a conformal prototype-based classifier for dissimilarity data to semi-supervised tasks. A ‘secure region’ of unlabeled data is identified to improve the trained model based on labeled data and to adapt the model complexity. The new approach (i) can directly deal with arbitrary symmetric dissimilarity matrices, (ii) offers intuitive classification by sparse prototypes, (iii) adapts the model complexity. Experiments confirm the effectiveness of our approach in comparison to state-of-the-art methods.
Original languageEnglish
Pages (from-to)138-145
JournalPattern Recognition Letters
Early online date23 Jul 2014
Publication statusPublished - 1 Nov 2014


  • Semi-supervised learning
  • Proximity data
  • Dissimilarity data
  • Conformal prediction
  • Generalized Learning Vector Quantization


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