Learning Robust Low-Rank Approximation for Crowdsourcing on Riemannian Manifold

https://doi.org/10.1016/j.procs.2017.05.179Get rights and content
Under a Creative Commons license
open access

Abstract

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 l2,1-norm. The proposed ROLA can improve the aggregation performance when compared with state-of-the-art crowdsourcing methods.

Keywords

Crowdsourcing
Low-Rank
Riemannian Optimization

Cited by (0)