Dimensionality Reduction

Linear Discriminant Analysis

Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.

Extracted from Wikipedia


Paper: Linear Discriminant Analysis: A Detailed Tutorial

Public version: Linear Discriminant Analysis: A Detailed Tutorial


Paper Code Results Date Stars


Task Papers Share
Topic Models 90 14.90%
General Classification 59 9.77%
Classification 37 6.13%
Dimensionality Reduction 34 5.63%
Clustering 32 5.30%
Test 22 3.64%
Sentiment Analysis 19 3.15%
Retrieval 17 2.81%
Image Classification 10 1.66%


Component Type
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