Principal Component Analysis: Properties and Applications

In course

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Description

This lecture covers the theory and applications of Principal Component Analysis (PCA) in multivariate statistics. It explains the definition and properties of principal components, including population and sample principal components. The lecture discusses the correlation structure, choice of components, and graphical representations. Additionally, it explores sampling properties, hypothesis testing, and the use of PCA in regression analysis.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_jgi4hski

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Statistical hypothesis testing

Data analysis: Regression analysis

Data collection: Statistical sampling

Mathematics

Algebra: Linear algebra

Information engineering

Natural language processing: Topics in natural language processing

Data science: Dimensionality reduction

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