KLDA - An Iterative Approach to Fisher Discriminant Analysis
Date
2007
Authors
Lu, Fangfang
Li, Hongdong
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE Inc)
Abstract
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 Kullback-Leibler divergence is adopted to measure the disparity between the distributions of each pair of classes. Our proposed algorithm can be naturally extended to handle nonlinear data by exploiting the kernel trick. Experimental results on the real world databases demonstrate the effectiveness of both the linear and kernel versions of our algorithm.
Description
Keywords
Keywords: Feature transformations; Fisher discriminant analysis; Function optimization; International conferences; Iterative approaches; Kernel fisher discriminant analysis; Kernel tricks; Kullback-Leibler divergence; Linear discriminant analysis; Nonlinear data; N Kernel fisher discriminant analysis; Kullback-Leibler divergence; Linear discriminant analysis; Optimization
Citation
Collections
Source
Proceedings of the 2007 IEEE International Conference on Image Processing (ICIP-2007)
Type
Conference paper
Book Title
Entity type
Access Statement
License Rights
Restricted until
2037-12-31
Downloads
File
Description