  The real-world data usually exhibits heterogeneous properties such as
modalities, views, or resources, which brings some unique challenges wherein
the key is Heterogeneous Representation Learning (HRL) termed in this paper.
This brief survey covers the topic of HRL, centered around several major
learning settings and real-world applications. First of all, from the
mathematical perspective, we present a unified learning framework which is able
to model most existing learning settings with the heterogeneous inputs. After
that, we conduct a comprehensive discussion on the HRL framework by reviewing
some selected learning problems along with the mathematics perspectives,
including multi-view learning, heterogeneous transfer learning, Learning using
privileged information and heterogeneous multi-task learning. For each learning
task, we also discuss some applications under these learning problems and
instantiates the terms in the mathematical framework. Finally, we highlight the
challenges that are less-touched in HRL and present future research directions.
To the best of our knowledge, there is no such framework to unify these
heterogeneous problems, and this survey would benefit the community.
