Learning Robust Low-Rank Approximation for Crowdsourcing on Riemannian Manifold
Qian Li, Zhichao Wang, Gang Li, Yanan Cao, Gang Xiong, Li Guo · Procedia Computer Science · 2017
Recently, crowdsourcing has attracted substantial research interest due to its efficiency in collecting labels for machine learning and computer vision tasks. This paper proposes a Rieman-nian manifold optimization algorithm, ROLA ( Robust Low-rank Approximation ), to aggregate the labels from a novel perspective. Specifically, a novel low-rank approximation model is proposed to capture underlying correlation among annotators meanwhile identify annotator-specific noise. More significantly, ROLA defines the label noise in crowdsourcing as annotator-specific noise, which can be well regularized by l 2 , 1 -norm. The proposed ROLA can improve the aggregation performance when compared with state-of-the-art crowdsourcing methods.