Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4960959 | Procedia Computer Science | 2017 | 10 Pages |
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
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Qian Li, Zhichao Wang, Gang Li, Yanan Cao, Gang Xiong, Li Guo,