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KLDA - An Iterative Approach to Fisher Discriminant Analysis

Lu, Fangfang; Li, Hongdong

Description

In this paper, we present an iterative approach to Fisher discriminant analysis called Kullback-Leibler discriminant analysis (KLDA) for both linear and nonlinear feature extraction. We pose the conventional problem of discriminative feature extraction into the setting of function optimization and recover the feature transformation matrix via maximization of the objective function. The proposed objective function is defined by pairwise distances between all pairs of classes and the...[Show more]

CollectionsANU Research Publications
Date published: 2007
Type: Conference paper
URI: http://hdl.handle.net/1885/26076
Source: Proceedings of the 2007 IEEE International Conference on Image Processing (ICIP-2007)
DOI: 10.1109/ICIP.2007.4379127

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