The class-specific method for classification
Paul M. Baggenstoss · The Journal of the Acoustical Society of America · 2003
This talk describes a new probabilistic method for classification called the ‘‘class-specific method’’ (CSM). CSM is able to avoid the ‘‘curse of dimensionality’’ which plagues most classifiers which attempt to determine the decision boundaries in a high-dimensional feature space. Using CSM, it is possible to build a theoretically optimum classifier 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 classical classification theory to handle multiple feature spaces. It is completely general, and requires no simplifying assumption such as Gaussianity or that data lies in linear subspaces. In real-data problems, CSM has shown orders of magnitude reductions in the false-alarm rate. CSM achieves this gain because it is able to make use of partial prior knowledge about the data classes. In contrast, the existing theory can only make use of full knowledge—that is when the parametric forms of the data probability density functions (PDFs) are known.