A dynamic mapping based on probabilistic relaxation

A dynamic mapping is presented, which is defined by a set of iterative equations. It is shown that the mapping maps the domain space which is composed of all m-dimensional probabilistic vectors to a space which is composed of the m basic unit vectors of the m-dimensional Euclidean space and m(m-1)/spl middot//spl middot//spl middot/(m-j+1)/j! m-dimensional probabilistic vectors in which some components of each probabilistic vector are zero and the remainder are identical. Thus, the dynamic mapping maps the domain space with an infinite number of states to the mapping space which has a finite number of states. The proposed mapping provides an effective classification or cluster scheme when the features of the considered object or data are described by a probabilistic vector.