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Online Matrix Factorization for Space Embedding Multilabel Annotation

  • Sebastian Otálora-Montenegro
  • Santiago A. Pérez-Rubiano
  • Fabio A. González
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

The paper presents an online matrix factorization algorithm for multilabel learning. This method addresses the multi-label annotation problem finding a joint embedding that represents both instances and labels in a common latent space. An important characteristic of the novel method is its scalability, which is a consequence of its formulation as an online learning algorithm. The method was systematically evaluated in different standard datasets and compared against state-of-the-art space embedding multi-label learning algorithms showing competitive results.

Keywords

Compressive Sensing Canonical Correlation Analysis Reconstruction Function Label Representation Online Learning Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sebastian Otálora-Montenegro
    • 1
  • Santiago A. Pérez-Rubiano
    • 1
  • Fabio A. González
    • 1
  1. 1.MindLab Research GroupUniversidad Nacional de ColombiaBogotáColombia

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