  This work is motivated by a question at the heart of unsupervised learning
approaches: Assume we are collecting a number K of (subjective) opinions about
some event E from K different agents. Can we infer E from them? Prima facie
this seems impossible, since the agents may be lying. We model this task by
letting the events be distributed according to some distribution p and the task
is to estimate p under unknown noise. Again, this is impossible without
additional assumptions. We report here the finding of very natural such
assumptions - the availability of multiple copies of the true data, each under
independent and invertible (in the sense of matrices) noise, is already
sufficient: If the true distribution and the observations are modeled on the
same finite alphabet, then the number of such copies needed to determine p to
the highest possible precision is exactly three! This result can be seen as a
counterpart to independent component analysis. Therefore, we call our approach
'dependent component analysis'. In addition, we present generalizations of the
model to different alphabet sizes at in- and output. A second result is found:
the 'activation' of invertibility through multiple parallel uses.
