Visualization of Complex Datasets with the Self-Organizing Spanning Tree
Visualization of real world data is a difficult task due to the high-dimensional and the complex structure in real datasets. Scientific data visualization requires a variety of mathematical techniques to transform high-dimensional data sets into simple graphical objects that provide a clearer understanding. In this work a Self-Organizing Spanning Tree is proposed, which is able to learn a tree topology without any prespecified structure. Experimental results are provided to show the good performance with synthetic and real data. Moreover, the proposed self-organizing model is applied to color vector quantization, whose comparative results are provided.
KeywordsSelf-organizing map topologies Visualization Spanning trees Unsupervised learning
Unable to display preview. Download preview PDF.
- 1.Kohonen, T.: Self-Organizing Maps, 3nd edn. Springer (2001)Google Scholar
- 3.Yin, H.: The self-organizing maps: Background, theories, extensions and applications. Studies in Computational Intelligence 115, 715–762 (2008)Google Scholar
- 12.Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: The SSIM Index for Image Quality Assessment (2003). https://ece.uwaterloo.ca/ z70wang/research/ssim/ (accessed January 31, 2015)