PCA and Kernel PCA

In course

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Description

This lecture covers Principal Component Analysis (PCA) and Kernel PCA, explaining how PCA is used to eliminate dimensions by finding the principal components with the most variation. It also delves into the comparison between PCA and Kernel PCA, showcasing how Kernel PCA projects data into a higher-dimensional space to make it linearly separable.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_sy2fdvcx

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Ontological neighbourhood

Information engineering

Data science: Dimensionality reduction

Machine learning: Topics in machine learning

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