Abstract
This paper presents privileged multi-label learning
(PrML) to explore and exploit the relationship between labels in multi-label learning problems. We
suggest that for each individual label, it cannot only
be implicitly connected with other labels via the
low-rank constraint over label predictors, but also
its performance on examples can receive the explicit
comments from other labels together acting as an
Oracle teacher. We generate privileged label feature
for each example and its individual label, and then
integrate it into the framework of low-rank based
multi-label learning. The proposed algorithm can
therefore comprehensively explore and exploit label
relationships by inheriting all the merits of privileged information and low-rank constraints. We
show that PrML can be efficiently solved by dual
coordinate descent algorithm using iterative optimization strategy with cheap updates. Experiments
on benchmark datasets show that through privileged
label features, the performance can be significantly
improved and PrML is superior to several competing
methods in most cases