## Dependent Component Analysis

We present an information-theoretic analysis of a question right at the heart of unsupervised learning approaches:

Assume we are collecting a number K of observations about some event E from K different agents. Can we infer E from them without exactly knowing the behaviour of each of the agents? We model this task by letting the events be distributed according to a distribution p and the task is to estimate p under unknown and independent noise. It turns out that this task is feasible if