Title: Privileged Multi-label Learning,
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 \emph{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.