  We present a novel method for solving Canonical Correlation Analysis (CCA) in
a sparse convex framework using a least squares approach. The presented method
focuses on the scenario when one is interested in (or limited to) a primal
representation for the first view while having a dual representation for the
second view. Sparse CCA (SCCA) minimises the number of features used in both
the primal and dual projections while maximising the correlation between the
two views. The method is demonstrated on two paired corpuses of English-French
and English-Spanish for mate-retrieval. We are able to observe, in the
mate-retreival, that when the number of the original features is large SCCA
outperforms Kernel CCA (KCCA), learning the common semantic space from a sparse
set of features.
