Linear Discriminant Analysis

Linear Discriminant Analysis ( LDA) is a technique of dimension reduction used in pattern recognition , machine learning and statistics to find a linear combination of characteristics that characterizes or separates two groups or more of artifacts or events. Since the name implies dimensionality reduction techniques decrease the number of dimensions that are variables in a dataset while preserving as much information as possible. Linear Discriminant Analysis, or LDA, uses the information from both features to construct a new axis and transfers the data onto the new axis to minimize the variance and increase the distance between the means of the new axis in such a way as to minimize the variance and maximize the difference between the two groups. The advantage of LDA is that it uses understanding from both characteristics to form a new alignment, which in turn minimizes the variation and maximizes the distance between two variables.

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