Tech moves fast! Stay ahead of the curve with Techopedia!
Join nearly 200,000 subscribers who receive actionable tech insights from Techopedia.
Linear discriminant analysis (LDA) is a type of linear combination, a mathematical process using various data items and applying functions to that set to separately analyze multiple classes of objects or items. Flowing from Fisher's linear discriminant, linear discriminant analysis can be useful in areas like image recognition and predictive analytics in marketing.
The fundamental idea of linear combinations goes back as far as the 1960s with the Altman Z-scores for bankruptcy and other predictive constructs. Now, linear discriminant analysis helps to represent data for more than two classes, when logic regression is not sufficient. Linear discriminant analysis takes the mean value for each class and considers variants in order to make predictions assuming a Gaussian distribution. It is one of several types of algorithms that is part of crafting competitive machine learning models.