Linear Discriminant Analysis

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What Does Linear Discriminant Analysis Mean?

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.

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Techopedia Explains Linear Discriminant Analysis

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.

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Margaret Rouse
Senior Editor
Margaret Rouse
Senior Editor

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.