Thrun,M.C., Lerch,F., Lotsch,J., Ultsch,A.
Abstract:
Dimensionality reduction by feature extraction is commonly used to transform high-dimensional data into a low dimensional space. With the aim to create a visualization of data, only projections onto two dimensions are considered here. Self-organizing maps were chosen as the projection method, which enabled the use of the U*-Matrix as an established method to visualize data as landscapes. Owing to the availability of the 3D printing technique, this allows presenting the structure of data in an intuitive way. For this purpose, information about the height of the landscapes is used to produce a three dimensional landscape with a 3D color printer. Similarities between high-dimensional data are observed as valleys and dissimilarities as mountains or ridges. These 3D prints provide topical experts a haptic grasp of high-dimensional structures. The method will be exemplary demonstrated on multivariate data comprising pain-related bio responses. In addition, a new R package “Umatrix” is introduced that allows the user to generate landscapes with hypsometric tints.