Title: Multi-Label Learning with Global and Local Label Correlation,
Abstract: It is well-known that exploiting label correlations is important to
multi-label learning. Existing approaches either assume that the label
correlations are global and shared by all instances; or that the label
correlations are local and shared only by a data subset. In fact, in the
real-world applications, both cases may occur that some label correlations are
globally applicable and some are shared only in a local group of instances.
Moreover, it is also a usual case that only partial labels are observed, which
makes the exploitation of the label correlations much more difficult. That is,
it is hard to estimate the label correlations when many labels are absent. In
this paper, we propose a new multi-label approach GLOCAL dealing with both the
full-label and the missing-label cases, exploiting global and local label
correlations simultaneously, through learning a latent label representation and
optimizing label manifolds. The extensive experimental studies validate the
effectiveness of our approach on both full-label and missing-label data.