Title: Subset Labeled LDA for Large-Scale Multi-Label Classification,
Abstract: Labeled Latent Dirichlet Allocation (LLDA) is an extension of the standard
unsupervised Latent Dirichlet Allocation (LDA) algorithm, to address
multi-label learning tasks. Previous work has shown it to perform in par with
other state-of-the-art multi-label methods. Nonetheless, with increasing label
sets sizes LLDA encounters scalability issues. In this work, we introduce
Subset LLDA, a simple variant of the standard LLDA algorithm, that not only can
effectively scale up to problems with hundreds of thousands of labels but also
improves over the LLDA state-of-the-art. We conduct extensive experiments on
eight data sets, with label sets sizes ranging from hundreds to hundreds of
thousands, comparing our proposed algorithm with the previously proposed LLDA
algorithms (Prior--LDA, Dep--LDA), as well as the state of the art in extreme
multi-label classification. The results show a steady advantage of our method
over the other LLDA algorithms and competitive results compared to the extreme
multi-label classification algorithms.