Using Both Latent and Supervised Shared Topics for Multitask Learning

摘要

This paper introduces two new frameworks, Doubly Supervised Latent Dirichlet Allocation (DSLDA) and its non-parametric variation (NP-DSLDA), that integrate two different types of supervision: topic labels and category labels. This approach is particularly useful for multitask learning, in which both latent and supervised topics are shared between multiple categories. Experimental results on both document and image classification show that both types of supervision improve the performance of both DSLDA and NP-DSLDA and that sharing both latent and supervised topics allows for better multitask learning.