Applications of LDA

What is Linear Discriminant Analysis?

Linear Discriminant Analysis (LDA), also known as Normal Discriminant Analysis or Discriminant Function Analysis, is a dimensionality reduction technique primarily utilized in supervised classification problems. It facilitates the modeling of distinctions between groups, effectively separating two or more classes. LDA operates by projecting features from a higher-dimensional space into a lower-dimensional one. In machine learning, LDA serves as a supervised learning algorithm specifically designed for classification tasks, aiming to identify a linear combination of features that optimally segregates classes within a dataset....

Assumptions of LDA

LDA assumes that the data has a Gaussian distribution and that the covariance matrices of the different classes are equal. It also assumes that the data is linearly separable, meaning that a linear decision boundary can accurately classify the different classes....

How does LDA work?

LDA works by projecting the data onto a lower-dimensional space that maximizes the separation between the classes. It does this by finding a set of linear discriminants that maximize the ratio of between-class variance to within-class variance. In other words, it finds the directions in the feature space that best separates the different classes of data....

Python Code Implementation of LDA

In this implementation, we will perform linear discriminant analysis using the Scikit-learn library on the Iris dataset....

Advanatages & Disadvantages of using LDA

Advanatages of using LDA...

Applications of LDA

Face Recognition: In the field of Computer Vision, face recognition is a very popular application in which each face is represented by a very large number of pixel values. Linear discriminant analysis (LDA) is used here to reduce the number of features to a more manageable number before the process of classification. Each of the new dimensions generated is a linear combination of pixel values, which form a template. The linear combinations obtained using Fisher’s linear discriminant are called Fisher’s faces.Medical: In this field, Linear discriminant analysis (LDA) is used to classify the patient’s disease state as mild, moderate, or severe based on the patient’s various parameters and the medical treatment he is going through. This helps the doctors to intensify or reduce the pace of their treatment.Customer Identification: Suppose we want to identify the type of customers who are most likely to buy a particular product in a shopping mall. By doing a simple question and answers survey, we can gather all the features of the customers. Here, a Linear discriminant analysis will help us to identify and select the features which can describe the characteristics of the group of customers that are most likely to buy that particular product in the shopping mall....