Class-specific feature sets in classification
Paul M. Baggenstoss · IEEE Transactions on Signal Processing · 1999
In this correspondence, we present a new approach to the design of probabilistic classifiers that circumvents the dimensionality problem. Rather than working with a common high-dimensional feature set, the classifier is written in terms of likelihood ratios with respect to a common class using sufficient statistics chosen specifically for each class.