  In this paper, we propose a novel approach for learning multi-label
classifiers with the help of privileged information. Specifically, we use
similarity constraints to capture the relationship between available
information and privileged information, and use ranking constraints to capture
the dependencies among multiple labels. By integrating similarity constraints
and ranking constraints into the learning process of classifiers, the
privileged information and the dependencies among multiple labels are exploited
to construct better classifiers during training. A maximum margin classifier is
adopted, and an efficient learning algorithm of the proposed method is also
developed. We evaluate the proposed method on two applications: multiple object
recognition from images with the help of implicit information about object
importance conveyed by the list of manually annotated image tags; and multiple
facial action unit detection from low-resolution images augmented by
high-resolution images. Experimental results demonstrate that the proposed
method can effectively take full advantage of privileged information and
dependencies among multiple labels for better object recognition and better
facial action unit detection.
