The Class-Specific Classifier: Avoiding the Curse of Dimensionality Tutorial
Abstract
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 lies in linear subspaces.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 15, 2003
- Accession Number
- ADA477364
Entities
People
- Paul Baggenstoss
Organizations
- Naval Undersea Warfare Center