Incremental Generalized Discriminative Common Vectors for Image Classification

Katerine Diaz-Chito, Francesc J. Ferri, Wladimiro Diaz-Villanueva

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)


© 2012 IEEE. Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model.
Original languageEnglish
Article number6906266
Pages (from-to)1761-1775
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number8
Publication statusPublished - 1 Aug 2015


  • Generalized discriminative common vector (GDCV)
  • image classification
  • incremental learning
  • null space
  • subspace-based methods


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