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How is linear discriminant analysis used in machine learning?
Linear discriminant analysis ( LDA ), normal discriminant analysis ( NDA ), or discriminant function analysis is a generalization of Fisher’s linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events.
What is the difference between LDA and Fisher’s linear discriminant?
Fisher’s linear discriminant. The terms Fisher’s linear discriminant and LDA are often used interchangeably, although Fisher’s original article actually describes a slightly different discriminant, which does not make some of the assumptions of LDA such as normally distributed classes or equal class covariances.
How is linear discriminant analysis used in face recognition?
In computerised face recognition, each face is represented by a large number of pixel values. Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification.
When to use LDA and discriminant correspondence analysis?
LDA works when the measurements made on independent variables for each observation are continuous quantities. When dealing with categorical independent variables, the equivalent technique is discriminant correspondence analysis.
Which is the simplest type of discriminant analysis?
This means that if you could plot the data, each class would form an ellipsoid, but the means would differ. The simplest type of discriminant analysis is called linear discriminant analysis or LDA. The LDA model is appropriate when the ellipsoids have the same orientation as shown below.
When do you use Quadratic discriminant analysis?
A related method called quadratic discriminant analysis is appropriate when the ellipsoids have different orientations. To understand how the method works, consider the 2 ellipses in Figure 1a, which have the same orientation and spread.