  We study the problem of aggregation noisy labels. Usually, it is solved by
proposing a stochastic model for the process of generating noisy labels and
then estimating the model parameters using the observed noisy labels. A
traditional assumption underlying previously introduced generative models is
that each object has one latent true label. In contrast, we introduce a novel
latent distribution assumption, implying that a unique true label for an object
might not exist, but rather each object might have a specific distribution
generating a latent subjective label each time the object is observed. Our
experiments showed that the novel assumption is more suitable for difficult
tasks, when there is an ambiguity in choosing a "true" label for certain
objects.
