Title: | Visualization and 3D printing of multivariate data of biomarkers |
Authors: | Thrun, Michael C. Lerch, Florian |
Citation: | WSCG '2016: short communications proceedings: The 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech RepublicMay 30 - June 3 2016, p. 7-16. |
Issue Date: | 2016 |
Publisher: | Václav Skala - UNION Agency |
Document type: | konferenční příspěvek conferenceObject |
URI: | wscg.zcu.cz/WSCG2016/!!_CSRN-2602.pdf http://hdl.handle.net/11025/29682 |
ISBN: | 978-80-86943-58-9 |
ISSN: | 2464-4617 |
Keywords: | samoorganizační mapa;multivariační vizualizace dat;snížení dimenze;vysokorozměrná data;3D tisk;u matice |
Keywords in different language: | self-organizing map;multivariate data visualization;dimensionality reduction;high dimensional data;3D printing;u-matrix |
Abstract: | Dimensionality reduction by feature extraction is commonly used to project high-dimensional data into a lowdimensional 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 exemplarily 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. |
Rights: | © Václav Skala - UNION Agency |
Appears in Collections: | WSCG '2016: Short Papers Proceedings |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/29682
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