Contents
Which is the best description of linear discriminant analysis?
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 and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events.
How is discriminant analysis different from factor analysis?
Discriminant analysis is also different from factor analysis in that it is not an interdependence technique: a distinction between independent variables and dependent variables (also called criterion variables) must be made. LDA works when the measurements made on independent variables for each observation are continuous quantities.
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.
When to use discriminant analysis for categorical independent variables?
When dealing with categorical independent variables, the equivalent technique is discriminant correspondence analysis. Discriminant analysis is used when groups are known a priori (unlike in cluster analysis). Each case must have a score on one or more quantitative predictor measures, and a score on a group measure.
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.
Which is an example of a discriminant problem?
Discriminant analysis is a classification problem, where two or more groups or clusters or populations are known a priori and one or more new observations are classified into one of the known populations based on the measured characteristics. Let us look at three different examples.
When to use LDA as a discriminant function?
LDA arises in the case where we assume equal covariance among K classes, i.e. Σ1 = Σ2 = ⋯ = ΣK. Then we can obtain the following discriminant function: δ k(x) = xTΣ − 1μk − 1 2μTkΣ − 1μk + logπ k, using the Gaussian distribution likelihood function. This is a linear function in x.
What is the name of the discriminant function?
The above function is called the discriminant function. Note the use of log-likelihood here. In another word, the discriminant function tells us how likely data x is from each class. The decision boundary separating any two classes, k and l, therefore, is the set of x where two discriminant functions have the same value.
How to do discriminant analysis using LDA or PCA?
When doing discriminant analysis using LDA or PCA it is straightforward to plot the projections of the data points by using the two strongest factors. This can be done in R by using the x component of the pca object or the x component of the prediction lda object.
How to plot QDA projections using LDA or PCA?
Plotting QDA projections in R. When doing discriminant analysis using LDA or PCA it is straightforward to plot the projections of the data points by using the two strongest factors. This can be done in R by using the x component of the pca object or the x component of the prediction lda object.