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ESANN
2007

A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning

8 years 10 months ago
A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning
Abstract. Canonical Correlation Analysis(CCA) is a useful tool to discover relationship between different sources of information represented by vectors. The solution of the underlying optimisation problem involves a generalised eigenproblem and is nonconvex. We will show a sequence of transformations which turn CCA into a convex maximum margin problem. The new formulation can be applied for the same class of problems at a significantly lower computational cost and with a better numerical stability.
Sándor Szedmák, Tijl De Bie, David R
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ESANN
Authors Sándor Szedmák, Tijl De Bie, David R. Hardoon
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