In recent years the tangent distance approach of Simard et al (1993) to
pattern recognition tasks such as handwritten character recognition has
proven successful. Simard's distance measure could be made locally invariant to any set of transformations of the input and when implemented in
one-nearest-neighbour classification of handwritten digits, outperformed all
other classification schemes.
Hastie et al (1996) propose prototype models which generalise the concept
of a centroid of a set of images in the Euclidian metric to a low-dimensional
hyperplane in the tangent metric, and these prototypes can be used to
reduce lookup time in classification.
We propose to apply and extend the tangent distance approach to classify
a set of body surface maps, which are recordings of the electrical activity
of the heart, of a large number of patients with various cardiac conditions.
Using a grid of p electrodes attached to the anterior chest, we calculate a
number of p-dimensional observation vectors for each patient and classify
input maps on the basis of overall distance of map to prototypes derived
from training set maps over all included observation vectors.
History
Publication
Proceedings of the 14th International Workshop on Statistical Modelling;