Separable linear discriminant classification

Authors:
Christian Bauckhage;John K. Tsotsos
Affiliations:
Centre for Vision Research, York University, Toronto, ON;Centre for Vision Research, York University, Toronto, ON
Venue:
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Year:
2005

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Abstract

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.