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

Source:

Paper: Linear Discriminant Analysis: A Detailed Tutorial

Public version: Linear Discriminant Analysis: A Detailed Tutorial

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Topic Models 84 20.64%
General Classification 59 14.50%
Dimensionality Reduction 25 6.14%
Sentiment Analysis 16 3.93%
Feature Selection 10 2.46%
Image Classification 9 2.21%
Document Classification 9 2.21%
Text Classification 7 1.72%
EEG 6 1.47%

Components


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

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