Linear Discriminant Analysis (LDA) Applications
Discriminant analysis has been successfully used for many applications. As long as we can transform the problem into a classification problem, we may apply the technique. You can use Discriminant analysis for original applications if you have new additional combination of features and objects that may never been considered by other people before. Here are a few fields and examples:
To identify type of customers that is likely to buy certain product in a store. Using simple questionnaires survey, we can get the features of customers. Discriminant analysis will help us to select which features can describe the group membership of buy or not buy the product.
Doctor diagnosing illness may be seen as which disease the patient has. However, we can transform this problem into classification problem by assigning the patient to a number of possible groups of disease based on the observation on the symptoms.
Question "Will it rain today" can be thought as prediction. Prediction problem can be thought as assigning "today" to one of the two possible groups of rain and dry.
To distinguish pedestrians from dogs and cars on captured image sequence of traffic data is a classification problem.
Scientists want to teach robot to learn to talk can be seen as classification problem. It assigns frequency, pitch, tune, and many other measurements of sound into many groups of words.
Preferable reference for this tutorial is
Teknomo, Kardi (2015) Discriminant Analysis Tutorial. http://people.revoledu.com/kardi/ tutorial/LDA/