Skip to Main Content
This article describes a new probabilistic method called the "class-specific method" (CSM). CSM has the potential to avoid the "curse of dimensionality" which plagues most classifiers which attempt to determine the decision boundaries in a high-dimensional feature space. In contrast, in CSM, it is possible to build classifiers without a common feature space. Separate low-dimensional features sets may be defined for each class, while the decision functions are projected back to the common raw data space. CSM effectively extends the classical classification theory to handle multiple feature spaces. It is completely general, and requires no simplifying assumption such as Gaussianity or that data ties in linear subspaces.