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Journal of Mathematical Imaging and Vision

, Volume 61, Issue 3, pp 331–351 | Cite as

Feature Extraction by Using Dual-Generalized Discriminative Common Vectors

  • Katerine Diaz-ChitoEmail author
  • Jesús Martínez del Rincón
  • Marçal Rusiñol
  • Aura Hernández-Sabaté
Article
  • 389 Downloads

Abstract

In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods.

Keywords

Online feature extraction Generalized discriminative common vectors Dual learning Incremental learning Decremental learning 

Notes

Acknowledgements

This work was supported by the project TIN2014-52072-P of the Spanish Ministry of Economy, Industry and Competitiveness with FEDER funds and the CERCA Programme/Generalitat de Catalunya.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Katerine Diaz-Chito
    • 1
    Email author
  • Jesús Martínez del Rincón
    • 2
  • Marçal Rusiñol
    • 1
  • Aura Hernández-Sabaté
    • 1
  1. 1.Centre de Visió per ComputadorUniversitat Autònoma de BarcelonaBarcelonaSpain
  2. 2.Centre for Secure Information TechnologiesQueen’s University BelfastBelfastUK

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