Evaluating dimensionality reduction techniques for visual category recognition using rényi entropy 19th European Signal Processing Conference (EUSIPCO 2011)
|Author(s):||Ashish Gupta ; Richard Bowden|
|Publisher:||IEEE - Institute of Electrical and Electronics Engineers, Inc.|
|Publication Date:||1 August 2011|
|Conference Location:||Barcelona, Spain|
|Conference Date:||29 August 2011|
|Page(s):||913 - 917|
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 rényi 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 neighborhood structure performed best amongst the techniques evaluated in this paper.