A Mutual Contamination Analysis of Mixed Membership and Partial Label Models

Understand

Many machine learning problems can be characterized by mutual contamination models.

• In these problems, one observes several random samples from different convex combinations of a set of unknown base distributions.
• It is of interest to decontaminate mutual contamination models, i.e., to recover the base distributions either exactly or up to a permutation.
• This paper considers the general setting where the base distributions are defined on arbitrary probability spaces.