Decision region connectivity analysis: A method for analyzing high-dimensional classifiers
In this paper we present a method to extract qualitative information from any classification model that uses decision regions to generalize (e.g., feed-forward neural nets, SVMs, etc). The method's complexity is independent of the dimensionality of the input data or model, making it computationally feasible for the analysis of even very high-dimensional models. The qualitative information extracted by the method can be directly used to analyze the classification strategies employed by a model, and also to compare strategies across different model types.
decision region classification decision boundary convex concave drca pca rule extraction high-dimensional visualization minimum volume ellipsoid