A non-parametric approach for learning from crowds

Jiayi Fu, Jinhong Zhong, Yunfeng Liu, Zhenyu Wang, Ke Tang

Abstract: Learning from crowds, which the labels of the instances are collected through crowdsourcing ways, has become an important research topic recently. Personal Classifier (PC) approach is a representative approach for learning from crowds due to its convex optimization formulation. PC approach makes assumptions about parameters' distribution, thus it is a parametric approach. However, these assumptions may not always hold, especially for real-world data sets. In this paper, we propose a new non-parametric approach, called NP approach, for learning from crowds. NP approach has a convex optimization formulation but without assumptions about parameters' distribution. In addition, NP approach can be generalized to the non-linear case directly, while the PC approach does not. Experimental studies show that NP approach outperforms other two compared approaches.