Improving Visualization of High-Dimensional Music Similarity Spaces

Visualizations of music databases are a popular form of interface allowing intuitive exploration of music catalogs. They are often based on lower dimensional projections of high dimensional music similarity spaces. Such similarity spaces have already been shown to be negatively impacted by so-called hubs and anti-hubs. These are points that appear very close or very far to many other data points due to a problem of measuring distances in high-dimensional spaces. We present an empirical study on how this phenomenon impacts three popular approaches to compute twodimensional visualizations of music databases. We also show how the negative impact of hubs and anti-hubs can be reduced by re-scaling the high dimensional spaces before low dimensional projection.