Optimization of the learning rate in the algorithm for data visualization - Medvedev, Viktor; Dzemyda, Gintautas (Lietuvos matematikos rinkinys, 2005)
In this paper, we discuss the visualization of multidimensional data. A well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one is Sammon‘s mapping. The paper describes an unsupervised backpropagation algorithm to train a multilayer feed-forward neural network (SAMANN) to perform the Sammon‘s nonlinear projection. In our research the emphasis is put on the optimization of the learning rate to save computation time without losing the mapping quality.