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.
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Bauckhage, C., Tsotsos, J.K. (2005). Separable Linear Discriminant Classification. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_40
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DOI: https://doi.org/10.1007/11550518_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28703-2
Online ISBN: 978-3-540-31942-9
eBook Packages: Computer ScienceComputer Science (R0)