New developments of nonlinear projections for the visualization of structures in nonvectorial data sets

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Teuvo Kohonen Name of the publication New Developments of Nonlinear Projections for the Visualization of Structures in Nonvectorial Data Sets Publisher School of Science Unit Department of Information and Computer Science Series Aalto University publication series SCIENCE + TECHNOLOGY 8/2011 Field of research Computer science Abstract New nonlinear projections for the visualization of structures in nonvectorial data sets are suggested. Since there exist problems with the convergence of the traditional multidimensional scaling (MDS) when the data are nonvectorial, a new version of the MDS, called the nearest-neighbors multidimensional scaling (NN-MDS), is introduced. While it represents the local data structures more accurately and converges fast, two amendments had to be added, in order to describe the global structures as well. A new initialization method called the GENINIT is also introduced. It is very fast and may be used as a nonlinear projection, too, but it is more suitable for the initialization of the more accurate learning algorithms.New nonlinear projections for the visualization of structures in nonvectorial data sets are suggested. Since there exist problems with the convergence of the traditional multidimensional scaling (MDS) when the data are nonvectorial, a new version of the MDS, called the nearest-neighbors multidimensional scaling (NN-MDS), is introduced. While it represents the local data structures more accurately and converges fast, two amendments had to be added, in order to describe the global structures as well. A new initialization method called the GENINIT is also introduced. It is very fast and may be used as a nonlinear projection, too, but it is more suitable for the initialization of the more accurate learning algorithms.