Issue #72 Theory Edwin Dong, Anurag Shinde
Principle Component Analysis (PCA), Simply Explained
Free editionPRO edition
This week’s PRO issue is accompanied by a Colab notebook on PCA. As always, the code in this issue can be executed on a normal home PC and does not require a large amount of VRAM.
Dealing with high-dimensional data is one of the biggest challenges in modern data science and machine learning. Images, text embeddings, genomic data, or sensor streams used as data can often contain thousands of features (input variables).
Such high-dimensional datasets carry a huge amount of information, but they also make computation more expensive, models harder to train, and results more difficult to interpret.
This is where dimensionality reduction comes in. The central idea of this concept is simple: can we represent the data with fewer variables while still retaining most of the important information?
One of the most powerful, widely used, and simple techniques for dimensionality reduction is Principal Component Analysis (PCA).
PCA is a linear mathematical method that transforms high-dimensional data into smaller-dimensional data with a set of new variables called principal components. These components are uncorrelated, and each component captures as much variance (spread) in the data as possible.
This edition is for PRO members.
