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.
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Source:
Paper: Linear Discriminant Analysis: A Detailed Tutorial
Public version: Linear Discriminant Analysis: A Detailed Tutorial
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Topic Models | 93 | 14.72% |
General Classification | 59 | 9.34% |
Classification | 39 | 6.17% |
Dimensionality Reduction | 34 | 5.38% |
Clustering | 32 | 5.06% |
Sentiment Analysis | 19 | 3.01% |
Retrieval | 17 | 2.69% |
EEG | 10 | 1.58% |
Sentence | 10 | 1.58% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |