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Published in:
2005 | OriginalPaper | Chapter
Separable Linear Discriminant Classification
Authors : Christian Bauckhage, John K. Tsotsos
Published in: Pattern Recognition
Publisher: Springer Berlin Heidelberg
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Linear discriminant analysis is a popular technique in computer vision, machine learning and data mining. It has been successfully applied to various problems, and there are numerous variations of the original approach. This paper introduces the idea of
separable
LDA. Towards the problem of binary classification for visual object recognition, we derive an algorithm for training separable discriminant classifiers. Our approach provides rapid training and runtime behavior and also tackles the small sample size problem. Experimental results show that the method performs robust and allows for online learning.