Visual category recognition is a difficult task of significant interest to the machine learning and vision community. One of the principal hurdles is the high dimensional feature space. This paper evaluates several linear and non-linear dimensionality reduction techniques. A novel evaluation metric, the renyi entropy of the inter-vector Euclidean distance distribution, is introduced. This information theoretic measure judges the techniques on their preservation of structure in lower-dimensional sub-space. The popular dataset, Caltech-101 is utilized in the experiments. The results indicate that the techniques which preserve local neighbourhood structure performed best amongst the techniques evaluated in this paper.