Definition
A discriminant is a function that takes an input variable x and outputs a class label y for it. A linear discriminant is a discriminant that uses a linear function of the input variables and more generally a linear function of some vector function of the input variables f(x).
This entry focuses on one such linear discriminant function called Fisher’s linear discriminant. Fisher’s discriminant works by finding a projection of input variables to a lower dimensional space while maintaining a class separability property.
Motivation and Background
The curse of dimensionality (Curse of Dimensionality) is an ongoing problem in applying statistical techniques to pattern recognition problems. Techniques that are computationally tractable in low-dimensional spaces can become completely impractical in high-dimensional spaces. Consequently, various methods have been proposed to reduce the dimensionality of the input or feature space in the hope of obtaining a more manageable problem....
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Most good statistical text books cover this.
Recommended Reading
Most good statistical text books cover this.
Bellman RE (1961) Adaptive control processes. Princeton University Press, Princeton
Bishop C (2006) Pattern recognition and machine learning. Springer, New York
Duda RO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New York
Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic, San Diego
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Quadrianto, N., Buntine, W.L. (2017). Linear Discriminant. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_480
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_480
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