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Data Subset Selection With Imperfect Multiple Labels

Meng Fang
Meng Fang
Tianyi Zhou
Tianyi Zhou
Jie Yin
Jie Yin
Yang Wang (王杨)
Yang Wang (王杨)
Dacheng Tao (陶大程)
Dacheng Tao (陶大程)

IEEE Transactions on Neural Networks and Learning Systems, pp. 2212-2221, 2018.

Cited by: 2|Bibtex|Views52|DOI:https://doi.org/10.1109/TNNLS.2018.2875470
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Other Links: pubmed.ncbi.nlm.nih.gov|academic.microsoft.com|dblp.uni-trier.de
Keywords:
LabelingNoise measurementData modelsCrowdsourcingApproximation algorithmsMore(2+)

Abstract:

We study the problem of selecting a subset of weakly labeled data where the labels of each data instance are redundant and imperfect. In real applications, less-than-expert labels are obtained at low cost in order to acquire many labels for each instance and then used for estimating the ground truth. However, on one side, preparing and pr...More

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