Learning robust low-rank approximation for crowdsourcing on Riemannian Manifold

© 2017 The Author(s). 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.