Principal component analysis (PCA) is the process of computing the principal components and using them to perform a change of basis on the data, sometimes using only the first few principal components and ignoring the rest.
Principal Component Analysis is most likely to appear on Científico de datos job descriptions where we found it mentioned 1,1 percent of the time.
What Is a Latent Space?
Towards Data Science - Medium
PCA & K-Means for Traffic Data in Python
Towards Data Science - Medium
The Math Behind “The Curse of Dimensionality”
Towards Data Science - Medium
Deep Dive into Sora’s Diffusion Transformer (DiT) by Hand ✍︎
Towards Data Science - Medium
Unlocking Insights: Random Forests for PCA and Feature Importance
Towards Data Science - Medium