Visualization of Support Vector Machines with Unsupervised Learning

The visualization of support vector machines in realistic settings is a difficult problem due to the high dimensionality of the typical datasets involved. However, such visualizations usually aid the understanding of the model and the underlying processes, especially in the biosciences. Here we propose a novel visualization technique of support vector machines based on unsupervised learning, specifically self-organizing maps. Conceptually, self-organizing maps can be thought of as neural networks that investigate a high-dimensional data space for clusters of data points and then project the clusters onto a two-dimensional map preserving the topologies of the original clusters as much as possible. This allows for the visualization of high-dimensional datasets together with their support vector models. With this technique we investigate a number of support vector machine visualization scenarios based on real world biomedical datasets