PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Simultaneous learning of a discriminative projection and prototypes for nearest-neighbor classification
Mauricio Villegas and Roberto Paredes
In: CVPR 2008, 24-26 Jun 2008, Anchorage, Alaska.


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.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:4598
Deposited By:Alfons Juan
Deposited On:24 March 2009