Dimensionality Reduction
Principal Components Analysis
Principle Components Analysis (PCA) is an unsupervised method primary used for dimensionality reduction within machine learning. PCA is calculated via a singular value decomposition (SVD) of the design matrix, or alternatively, by calculating the covariance matrix of the data and performing eigenvalue decomposition on the covariance matrix. The results of PCA provide a low-dimensional picture of the structure of the data and the leading (uncorrelated) latent factors determining variation in the data.
Image Source: Wikipedia
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Dimensionality Reduction | 123 | 19.81% |
| Clustering | 22 | 3.54% |
| Classification | 21 | 3.38% |
| regression | 18 | 2.90% |
| Anomaly Detection | 18 | 2.90% |
| Denoising | 15 | 2.42% |
| Image Classification | 14 | 2.25% |
| Fairness | 10 | 1.61% |
| Self-Supervised Learning | 9 | 1.45% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
