Learning Attributes from the Crowdsourced Relative Labels Tian Tian,† Ning Chen,‡ Jun Zhu† †Dept. of Comp. Sci. & Tech., CBICR Center, State Key Lab for Intell. Tech. & Systems ‡MOE Key lab of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology TNList, Tsinghua University, Beijing, China {tiant16@mails, ningchen@, dcszj@}tsinghua.edu.cn Abstract Finding semantic attributes to describe related concepts is typ- ically a hard problem. The commonly used attributes in most fields are designed by domain experts, which is expensive and time-consuming. In this paper we propose an efficient method to learn human comprehensible attributes with crowd- sourcing. We first design an analogical interface to collect relative labels from the crowds. Then we propose a hierar- chical Bayesian model, as well as an efficient initialization strategy, to aggregate labels and extract concise attributes. Our experimental results demonstrate promise on discovering
