Linear discriminant analysis

Linear discriminant analysis (LDA) is a statistical technique used in computer programs and applications, such as artificial intelligence (AI), for the analysis of data and classification of data points
into assigned groups. It is used as a supervised learning method for pattern recognition, and has wide applications in machine learning and in applications such as computer vision for automated image classification.

LDA is a feature extraction method, meaning it looks to identify underlying attributes in data that are important for classification. These attributes are condensed into a single linear combination of features, allowing for the minimization of data dimensions and easier interpretation of data when presented on charts and graphs. LDA provides a way of reducing the number of dimensions of a given dataset while preserving as much of the original information as possible.

Typically, LDA is used for binary classification wherein a new observation is determined to belong to one of two distinct classes. LDA can also be used for multiclass classification where classes are not mutually exclusive. Multi-class classification using LDA is beneficial because LDA not only helps reduce the amount of data processing, but it also improves the accuracy of classification.

LDA is a supervised technique, meaning that the required outcome (or dependent variable) is known before hand, and the variables are used to create a predictive model. This allows the model to be better calibrated to the expected outcomes. It also provides the ability to tune the model to achieve better accuracy.

In addition to its use in AI applications and computer vision, LDA may also be applied to natural language processing (NLP) for identifying sentiment in text. Specifically, NLP applications of LDA can be used to classify text into different sentiment classes, such as positive, negative, or neutral.

LDA represents an attractive option for many computer science and engineering projects due to its efficiency, low memory requirements, and improved classification accuracy. It can be used in combination with other methods, such as Support Vector Machines (SVM) or k-nearest neighbors (KNN), in order to increase performance. As a result, Linear Discriminant Analysis remains a popular choice for a wide range of data-driven applications.