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Linear Discriminant analysis has become the most widely used dimensionality reduction technique. This technique is used in machine learning as there are many different data sets that exist these days. This Discriminant analysis is created by Ronald A Fisher in 1936. However, the first developed Discriminant analysis has been used to solve only class-2 issues whereas the latest developed Discriminant analysis would be used to solve multi-class problems.
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This Discriminant analysis is used in machine learning for pre-processing the information and classifies the projects based on the patterns. When this analysis is used in Python, it helps to shrink the high dimensional data on the lower dimensional space. This analysis is mainly performed to reduce the dimensionality while saving the data based on class discrimination. The linear Discriminant analysis is used to find out the features of the linear combination. These features are used to characterize two or multiple objects or events. The combination that is obtained as a result would be used as a linear classifier that in turn can be used to reduce the dimensionality.
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The following is the data that is prepared to use for Linear Discriminant analysis:
As linear Discriminant analysis is very easy to use and simple so it is widely used in different variations and extensions. These are designed to improve the efficacy of Linear Discriminant analysis. A few of the examples where this LDA is used include:
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