---

Principal Component Analysis §

Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing. - Wikipedia

• SC - Lezione 12 - SVD Application for Latent Semantic Analysis and Principal Component Analysis (PCA)
• ML - Lezione 20 - Curse of Dimensionality, Data Dimensionality, Principal Component Analysis (PCA) and Nonlinear Principal Component Analysis (NPCA)
• ML2 - Lecture 5 - Hebbian Learning for Principal Component Analysis (TODO ultime due sezioni)

Main Idea §

• StatQuest: PCA main ideas in only 5 minutes!!!

Imagine you have a cluster of elements. Any element can be of a different type . We cannot observe differences from the outside.

These N elements have say, types so the data can be represented in tabular form (a Data Matrix).

If we have it is unfeasable to visualize through a graph to see positive or negative correalation between data.

PCA does solves this this problem by converting the correalations (“or the lacking of”) among cells into a 2D graph.

Another thing is that in a PCA plots, the axes are ranked in order of importance.