  We propose a unified framework for multi-view subspace learning to learn
individual orthogonal projections for all views. The framework integrates the
correlations within multiple views, supervised discriminant capacity, and
distance preservation in a concise and compact way. It not only includes
several existing models as special cases, but also inspires new novel models.
To demonstrate its versatility to handle different learning scenarios, we
showcase three new multi-view discriminant analysis models and two new
multi-view multi-label classification ones under this framework. An efficient
numerical method based on successive approximations via eigenvectors is
presented to solve the associated optimization problem. The method is built
upon an iterative Krylov subspace method which can easily scale up for
high-dimensional datasets. Extensive experiments are conducted on various
real-world datasets for multi-view discriminant analysis and multi-view
multi-label classification. The experimental results demonstrate that the
proposed models are consistently competitive to and often better than the
compared methods that do not learn orthogonal projections.
