Lecture

PCA: Directions of Largest Variance

Description

This lecture covers the concept of Principal Component Analysis (PCA) and its application in finding the directions of largest variance in a dataset. It explains how PCA can be used to reduce the dimensionality of data and visualize high-dimensional datasets. The lecture also discusses the process of decorrelating data using PCA, exploring the wine dataset, and the limitations of PCA in dimension reduction.

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Official source

https://mediaspace.epfl.ch/media/0_xth1ajs7

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Ontological neighbourhood

Information engineering

Data science: Dimensionality reduction

Signal processing: Data compression

Mathematics

Algebra: Linear algebra

Statistics

Statistical inference: Mathematical statistics

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