2008 IEEE Conference on Computer Vision and Pattern Recognition
Download PDF

Abstract

Computer vision and image recognition research have a great interest in dimensionality reduction techniques. Generally these techniques are independent of the classifier being used and the learning of the classifier is carried out after the dimensionality reduction is performed, possibly discarding valuable information. In this paper we propose an iterative algorithm that simultaneously learns a linear projection base and a reduced set of prototypes optimized for the Nearest-Neighbor classifier. The algorithm is derived by minimizing a suitable estimation of the classification error probability. The proposed approach is assessed through a series of experiments showing a good behavior and a real potential for practical applications.
Like what you’re reading?
Already a member?Sign In
Member Price
$11
Non-Member Price
$21
Add to CartSign In
Get this article FREE with a new membership!

Related Articles