Subset Labeled LDA for Large-Scale Multi-Label Classification

Understand

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.