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Hi I was wondering if we can compare Noisy label problem to a semi supervised approach? Also are there any papers on learning with noisy labels? Any help is appreciated.

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    $\begingroup$ Define what is considered a noisy label. Instance duplicates with different labels? $\endgroup$
    – inzl
    Aug 26 '14 at 13:06
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    $\begingroup$ Duplicates with different labels that makes the class space overly fragmented $\endgroup$
    – user48918
    Aug 26 '14 at 13:31
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    $\begingroup$ Yes this is possible. I've done this in one of my papers. Preprint freely available here. $\endgroup$ May 2 '15 at 13:22
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I would consider it as a semi-supervised learning problem. I would treat instances that have no contradicting duplicates as labelled instances, and instances with contradictory duplicates as unlabeled instances. Then it becomes a classical semi-supervised learning problem.

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http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9366/9948 Here is a paper start with initial noisy label data.

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