A Family of Canonical Correlation Networks

Authors:
P. L. Lai;C. Fyfe
Affiliations:
Applied Computational Intelligence Research Unit, The University of Paisley, Scotland;Applied Computational Intelligence Research Unit, The University of Paisley, Scotland
Venue:
Neural Processing Letters
Year:
2001

Citing 4
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An information-theoretic unsupervised learning algorithm for neural networks

An information-theoretic unsupervised learning algorithm for neural networks
A neural implementation of canonical correlation analysis

Neural Networks
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Learning perceptually salient visual parameters using spatiotemporal smoothness constraints

Neural Computation

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Abstract

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.