Learning from Noisy Side Information by Generalized Maximum Entropy Model
Tianbao Yang, Rong Jin, Anil Kumar Jain · 2010
We consider the problem of learning from noisy side information in the form of pairwise constraints. Although many algorithms have been developed to learn from side information, most of them assume perfect pairwise constraints. Given the pairwise constraints are often extracted from data sources such as paper citations, they tend to be noisy and inaccurate. In this paper, we introduce the generalization of maximum entropy model and propose a framework for learning from noisy side information based on the generalized maximum entropy model. The theoretic analysis shows that under certain assumption, theclassificationmodeltrainedfromthe noisy side information can be very close to theonetrainedfromthe perfectsideinformation. Extensive empirical studies verify the effectiveness of the proposed framework. 1.