  We establish a family of subspace-based learning method for multi-view
learning using the least squares as the fundamental basis. Specifically, we
investigate orthonormalized partial least squares (OPLS) and study its
important properties for both multivariate regression and classification.
Building on the least squares reformulation of OPLS, we propose a unified
multi-view learning framework to learn a classifier over a common latent space
shared by all views. The regularization technique is further leveraged to
unleash the power of the proposed framework by providing three generic types of
regularizers on its inherent ingredients including model parameters, decision
values and latent projected points. We instantiate a set of regularizers in
terms of various priors. The proposed framework with proper choices of
regularizers not only can recast existing methods, but also inspire new models.
To further improve the performance of the proposed framework on complex real
problems, we propose to learn nonlinear transformations parameterized by deep
networks. Extensive experiments are conducted to compare various methods on
nine data sets with different numbers of views in terms of both feature
extraction and cross-modal retrieval.
