  Multi-output learning aims to simultaneously predict multiple outputs given
an input. It is an important learning problem due to the pressing need for
sophisticated decision making in real-world applications. Inspired by big data,
the 4Vs characteristics of multi-output imposes a set of challenges to
multi-output learning, in terms of the volume, velocity, variety and veracity
of the outputs. Increasing number of works in the literature have been devoted
to the study of multi-output learning and the development of novel approaches
for addressing the challenges encountered. However, it lacks a comprehensive
overview on different types of challenges of multi-output learning brought by
the characteristics of the multiple outputs and the techniques proposed to
overcome the challenges. This paper thus attempts to fill in this gap to
provide a comprehensive review on this area. We first introduce different
stages of the life cycle of the output labels. Then we present the paradigm on
multi-output learning, including its myriads of output structures, definitions
of its different sub-problems, model evaluation metrics and popular data
repositories used in the study. Subsequently, we review a number of
state-of-the-art multi-output learning methods, which are categorized based on
the challenges.
