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


Paper Code Results Date Stars


Task Papers Share
Dimensionality Reduction 124 24.41%
General Classification 45 8.86%
Clustering 44 8.66%
Feature Selection 19 3.74%
Time Series 17 3.35%
Matrix Completion 11 2.17%
Denoising 10 1.97%
Anomaly Detection 8 1.57%
Face Recognition 8 1.57%


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