  This paper presents a robust matrix elastic net based canonical correlation
analysis (RMEN-CCA) for multiple view unsupervised learning problems, which
emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used
as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN
to distill naturally meaningful features without any prior assumption and to
measure effectively correlations between different 'views'. We can further
employ directly the kernel trick to extend the RMEN-CCA to the kernel scenario
with theoretical guarantees, which takes advantage of the kernel trick for
highly complicated nonlinear feature learning. Rather than simply incorporating
existing regularization minimization terms into CCA, this paper provides a new
learning paradigm for CCA and is the first to derive a coupled feature
selection based CCA algorithm that guarantees convergence. More significantly,
for CCA, the newly-derived RMEN-CCA bridges the gap between measurement of
relevance and coupled feature selection. Moreover, it is nontrivial to tackle
directly the RMEN-CCA by previous optimization approaches derived from its
sophisticated model architecture. Therefore, this paper further offers a bridge
between a new optimization problem and an existing efficient iterative
approach. As a consequence, the RMEN-CCA can overcome the limitation of CCA and
address large-scale and streaming data problems. Experimental results on four
popular competing datasets illustrate that the RMEN-CCA performs more
effectively and efficiently than do state-of-the-art approaches.
