Linear Discriminant Analysis (LDA) is a supervised learning algorithm used for classification tasks in machine learning.
LDA can handle correlation between features in the data. It is a technique used to find a linear combination of features that best separates the classes in a dataset. It is a simple and computationally efficient algorithm and can work well even when the number of features is much larger than the number of training samples.
LDA projects the data onto a lower-dimensional space that maximizes the separation between the classes. It achieves this by finding a set of linear discriminants that maximize the ratio of between-class variance to within-class variance.
LDA is used in face detection algorithms. In Fisherfaces, LDA is used to extract useful data from different faces. Coupled with eigenfaces, it produces effective results.
In the medical field, LDA has a great application in classifying the patient disease on the basis of various parameters of patient health and the medical treatment which is going on.
LDA can also be used for making predictions and so in decision making.
LDA is mainly applied for classification problems to classify the categorical output variable.