Since 2020, aggregated from related topics
Linear discriminant analysis (LDA) is a statistical technique used in machine learning and pattern recognition to classify data into distinct categories. It involves finding the linear combination of features that best separates different classes of data. LDA works by maximizing the ratio of between-class variance to within-class variance in order to find the best hyperplane that separates the data points in different classes. Overall, LDA is a powerful tool for dimensionality reduction and classification in various fields such as image recognition, speech recognition, and bioinformatics.