2013 | OriginalPaper | Buchkapitel
Online Matrix Factorization for Space Embedding Multilabel Annotation
verfasst von : Sebastian Otálora-Montenegro, Santiago A. Pérez-Rubiano, Fabio A. González
Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Verlag: Springer Berlin Heidelberg
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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.