The feature vectors of a data set encode information about relations between speaker groups, clusters and outliers. Based on the assumption that these relations are conserved within the spatial properties of feature vectors, we introduce similarity maps to visualize consistencies and deviations in magnitude and orientation between two feature vectors. We also present an iterative approach to find subspaces of a high-dimensional feature space that encode information about predefined speaker clusters. The methods were evaluated with two different data sets, one from chronically hoarse speakers and a second one from Parkinson’s Disease patients and a healthy control group. The results showed that similarity maps provide a decent visualization of speaker groups and the spatial properties of their respective feature vectors. With the iterative optimization, it was possible to find features that show pronounced spatial differences between predefined clusters.