Subset Labeled LDA for Large-Scale Multi-Label Classification

Yannis Papanikolaou; Grigorios Tsoumakas

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arXiv · 1709.05480

Subset Labeled LDA for Large-Scale Multi-Label Classification

Yannis Papanikolaou·Grigorios Tsoumakas

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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.

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BibTeX RIS

Yannis Papanikolaou, Grigorios Tsoumakas. 2017-09-16. Subset Labeled LDA for Large-Scale Multi-Label Classification. https://arxiv.org/abs/1709.05480

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