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.
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Keywords
- Compressive Sensing
- Canonical Correlation Analysis
- Reconstruction Function
- Label Representation
- Online Learning Algorithm
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Otálora-Montenegro, S., Pérez-Rubiano, S.A., González, F.A. (2013). Online Matrix Factorization for Space Embedding Multilabel Annotation. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41822-8_43
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DOI: https://doi.org/10.1007/978-3-642-41822-8_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41821-1
Online ISBN: 978-3-642-41822-8
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