A Family of Canonical Correlation Networks
We have previously introduced [2, 5] a neural implementation of Canonical Correlation Analysis (CCA). In this paper, we re-derive the learning method from a probabilistic perspective and then show that similar networks can be derived based on the pioneering work of Becker [1] if certain simplifying assumptions are made. Becker has shown that her network is able to find depth information from an abstraction of random dot stereogram data and so finally we note the similarity of the derived methods with those of Stone [3] which was used with a smooth stereo disparity data set to extract depth information.