What is linear discriminant analysis discuss with a suitable example?

Linear Discriminant Analysis or Normal Discriminant Analysis or Discriminant Function Analysis is a dimensionality reduction technique that is commonly used for supervised classification problems. It is used for modelling differences in groups i.e. separating two or more classes.

What does a linear discriminant analysis show?

Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification. Each of the new dimensions is a linear combination of pixel values, which form a template.

What does the linear discriminant function describe?

A linear discriminant function divides the feature space by a hyperplane decision surface. The orientation of the surface is determined by the normal vector w, and the location of the surface is determined by the bias w0.

Is linear discriminant analysis still used?

Linear discriminant analysis is used as a tool for classification, dimension reduction, and data visualization. It has been around for quite some time now. Despite its simplicity, LDA often produces robust, decent, and interpretable classification results.

What is the difference between PCA and LDA?

Both LDA and PCA are linear transformation techniques: LDA is a supervised whereas PCA is unsupervised – PCA ignores class labels. We can picture PCA as a technique that finds the directions of maximal variance: Remember that LDA makes assumptions about normally distributed classes and equal class covariances.

What are the decision boundaries for linear discriminant analysis?

It is linear if there exists a function H(x) = β0 + βT x such that h(x) = I(H(x) > 0). H(x) is also called a linear discriminant function. The decision boundary is therefore defined as the set {x ∈ Rd : H(x)=0}, which corresponds to a (d − 1)-dimensional hyperplane within the d-dimensional input space X.

What is the accuracy of the linear discriminant analysis model built on the dataset?

In this case, we can see that the model achieved a mean accuracy of about 89.3 percent. We may decide to use the Linear Discriminant Analysis as our final model and make predictions on new data.

How can the linear discriminant model be improved?

3.3. Improving LDA and other methods. When the subsets are ready, we could perform LDA on each individual subset instead of the entire data set, to greatly lessen the separation problem of LDA.

How many types of discriminant analysis are there?

The type which is used will be the 2-group Discriminant analysis. There are also some cases where the variable which is dependent has got about three or more categories in total. In those cases, the type which is used will be the multiple Discriminant analysis.