evaluating dimensionality reduction techniques for visual categorization using renyi entropy
Ashish Gupta and Richard Bowden
In: EUSIPCO 2011, 29 Aug - 02 Sep 2011, Barcelona, Spain.
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.
|EPrint Type:||Conference or Workshop Item (Poster)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Ashish Gupta|
|Deposited On:||06 February 2012|