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Lecture Notes in Computer Science
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 spacedoi:10.1007/978-3-642-41822-8_43 fatcat:mfeovm7pnneetmmit5hj2rxlkq