Refereed full papers (journals, book chapters, international conferences)

1997

@inproceedings{BKL1997,
	vgclass =	{refpap},
	vgproject =	{invariance},
	author =	{M. Borga and H. Knutsson and T. Landelius},
	title =	{Learning Canonical Correlations},
	booktitle =	{The 10th Scandinavian Conference on Image Analysis (SCIA'97)},
	address =	{Lappeenranta, Finland},
	pages =	{1--8},
	month =	{June},
	year =	{1997},
	abstract =	{This paper presents a novel learning algorithm that finds
	the linear combination of one set of multidimensional variates that is
	the best predictor, and at the same time finds the linear combination
	of another set which is the most predictable. This relation is known as
	the \emph{canonical correlation} and has the property of being
	invariant with respect to affine transformations of the two sets of
	variates. The algorithm successively finds all the canonical
	correlations beginning with the largest one. It is shown that canonical
	correlations can be used in computer vision to find feature detectors
	by giving examples of the desired features. When used on the pixel
	level, the method finds quadrature filters and when used on a higher
	level, the method finds combinations of filter output that are less
	sensitive to noise compared to vector averaging.},
}