Mapping error in the parallel realizations of SAMANN algorithm - Medvedev, Viktor; Dzemyda, Gintautas (Lietuvos matematikos rinkinys, 2004)
In this paper we discuss the visualization of multidimensional vectors. A well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one is Sammon's mapping. This algorithm preserves as well as possible all inter-pattern distances. We investigate an unsupervised back-propagation algorithm to train a multilayer feed-forward neural network (SAMANN) to perform the Sammoil's nonlinear projection and propose a parallel algorithm for SAMANN network.
Keywords: neutral networks; SAMANN algorithm; Sammon's mapping; visualization