It often happens that two features that were meant to measure different
characteristics are influenced by some common mechanism and tend to vary
together. For example, the perimeter and the maximum width of a figure will
both vary with scale; larger figures will have both larger perimeters and
larger maximum widths.
This degrades the perfomance of a classifier based on Euclidean distance
to a template. A pattern at the extreme of one class can be closer to the
template for another class than to its own template. A similar problem occurs
if features are badly scaled, for example, by measuing one feature in microns
and another in kilometers.
Solution: Use the Mahalanobis
metric (see ahead)
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