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