  Canonical correlation analysis (CCA) is a statistical learning method that
seeks to build view-independent latent representations from multi-view data.
This method has been successfully applied to several pattern analysis tasks
such as image-to-text mapping and view-invariant object/action recognition.
However, this success is highly dependent on the quality of data pairing (i.e.,
alignments) and mispairing adversely affects the generalization ability of the
learned CCA representations. In this paper, we address the issue of alignment
errors using a new variant of canonical correlation analysis referred to as
alignment-agnostic (AA) CCA. Starting from erroneously paired data taken from
different views, this CCA finds transformation matrices by optimizing a
constrained maximization problem that mixes a data correlation term with
context regularization; the particular design of these two terms mitigates the
effect of alignment errors when learning the CCA transformations. Experiments
conducted on multi-view tasks, including multi-temporal satellite image change
detection, show that our AA CCA method is highly effective and resilient to
mispairing errors.
