Decremental generalized discriminative common vectors applied to images classification

Katerine Diaz-Chito, Jesús Martínez del Rincón, Aura Hernández-Sabaté

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


© 2017 In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model.
Original languageEnglish
Pages (from-to)46-57
JournalKnowledge-Based Systems
Publication statusPublished - 1 Sep 2017


  • Classification
  • Decremental learning
  • Feature extraction
  • Generalized Discriminative Common Vectors
  • Linear subspace methods


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