Aggregating Crowd Wisdoms with Label-aware Autoencoders

Aggregating Crowd Wisdoms with Label-aware Autoencoders

Li'ang Yin, Jianhua Han, Weinan Zhang, Yong Yu

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 1325-1331. https://doi.org/10.24963/ijcai.2017/184

Aggregating crowd wisdoms takes multiple labels from various sources and infers true labels for objects. Recent research work makes progress by learning source credibility from data and roughly form three kinds of modeling frameworks: weighted majority voting, trust propagation, and generative models. In this paper, we propose a novel framework named Label-Aware Autoencoders (LAA) to aggregate crowd wisdoms. LAA integrates a classifier and a reconstructor into a unified model to infer labels in an unsupervised manner. Analogizing classical autoencoders, we can regard the classifier as an encoder, the reconstructor as a decoder, and inferred labels as latent features. To the best of our knowledge, it is the first trial to combine label aggregation with autoencoders. We adopt networks to implement the classifier and the reconstructor which have the potential to automatically learn underlying patterns of source credibility. To further improve inference accuracy, we introduce object ambiguity and latent aspects into LAA. Experiments on three real-world datasets show that proposed models achieve impressive inference accuracy improvement over state-of-the-art models.
Keywords:
Knowledge Representation, Reasoning, and Logic: Judgement Aggregation and Information Fusion
Machine Learning: Unsupervised Learning
Machine Learning: Neural Networks