  Crowdsourcing has become a primary means for label collection in many
real-world machine learning applications. A classical method for inferring the
true labels from the noisy labels provided by crowdsourcing workers is
Dawid-Skene estimator. In this paper, we prove convergence rates of a projected
EM algorithm for the Dawid-Skene estimator. The revealed exponent in the rate
of convergence is shown to be optimal via a lower bound argument. Our work
resolves the long standing issue of whether Dawid-Skene estimator has sound
theoretical guarantees besides its good performance observed in practice. In
addition, a comparative study with majority voting illustrates both advantages
and pitfalls of the Dawid-Skene estimator.
