2016/11/16 by Abhay Gupta, Gupta, Abhay
Computer Science · #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning and Data Classification #Mobile Crowdsensing and Crowdsourcing #cs.LG
paper · pdf · doi:10.48550/arxiv.1611.05340
8 pages, 1 figure
openalex publication_date 2016/11/16 · arxiv created 2016/11/17 · arxiv updated 2016/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
An important way to make large training sets is to gather noisy labels from crowds of non experts. We propose a method to aggregate noisy labels collected from a crowd of workers or annotators. Eliciting labels is important in tasks such as judging web search quality and rating products. Our method assumes that labels are generated by a probability distribution over items and labels. We formulate the method by drawing parallels between Gaussian Mixture Models (GMMs) and Restricted Boltzmann Machines (RBMs) and show that the problem of vote aggregation can be viewed as one of clustering. We use K-RBMs to perform clustering. We finally show some empirical evaluations over real datasets.