  In recent years, a great many methods of learning from multi-view data by
considering the diversity of different views have been proposed. These views
may be obtained from multiple sources or different feature subsets. In trying
to organize and highlight similarities and differences between the variety of
multi-view learning approaches, we review a number of representative multi-view
learning algorithms in different areas and classify them into three groups: 1)
co-training, 2) multiple kernel learning, and 3) subspace learning. Notably,
co-training style algorithms train alternately to maximize the mutual agreement
on two distinct views of the data; multiple kernel learning algorithms exploit
kernels that naturally correspond to different views and combine kernels either
linearly or non-linearly to improve learning performance; and subspace learning
algorithms aim to obtain a latent subspace shared by multiple views by assuming
that the input views are generated from this latent subspace. Though there is
significant variance in the approaches to integrating multiple views to improve
learning performance, they mainly exploit either the consensus principle or the
complementary principle to ensure the success of multi-view learning. Since
accessing multiple views is the fundament of multi-view learning, with the
exception of study on learning a model from multiple views, it is also valuable
to study how to construct multiple views and how to evaluate these views.
Overall, by exploring the consistency and complementary properties of different
views, multi-view learning is rendered more effective, more promising, and has
better generalization ability than single-view learning.
