Evaluating Dimensionality Reduction Techniques for Visual Category Recognition Using Rényi Entropy

Abstract

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.

Publication
In Processing of European Signal Processing Conference, EURASIP, Barcelona, Spain, pp. 913-917, September, 2011.
Date