  The classification problem of structured data can be solved with different
strategies: a supervised learning approach, starting from a labeled training
set, and an unsupervised learning one, where only the structure of the patterns
in the dataset is used to find a classification compatible with it. The two
strategies can be interpreted as extreme cases of a semi-supervised approach to
learn multi-view data, relevant for applications. In this paper I study the
critical properties of the two storage problems associated with these tasks, in
the case of the linear binary classification of doublets of points sharing the
same label, within replica theory. While the first approach presents a
SAT/UNSAT transition in a (marginally) stable replica-symmetric phase, in the
second one the satisfiability line lies in a full replica-symmetry-broken
phase. A similar behavior in the problem of learning with a margin is also
pointed out.
