Nonlinear Discriminative Data Visualization
Kerstin Bunte, Barbara Hammer, Petra Schneider, Michael L. Biehl · 2009
Abstract. Due to the tremendous increase of electronic information with respect to the size of data sets as well as dimensionality, visualization of high-dimensional data constitutes one of the key problems of data mining. Since embedding in lower dimensions necessarily includes a loss of information, methods to explicitly control the information kept by a specific visualization technique are highly desirable. The incorporation of supervised class information constitutes an important specific case. In this contribution we propose an extension of prototype-based local matrix learning by a charting technique which results in an efficient nonlinear dimension reduction and discriminative visualization of a given labelled data manifold. 1