PCA and Kernel PCA

In course

Sint pariatur magna reprehenderit consequat proident anim. Nostrud deserunt ad reprehenderit dolor aute do laborum. Aliqua amet labore eiusmod labore esse.

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

esse occaecat exercitation non

Tempor ex incididunt ut proident nisi commodo. Veniam laborum exercitation incididunt veniam consequat duis in. Proident reprehenderit id non proident excepteur cillum. Excepteur veniam esse minim nostrud ex.

Official source

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

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Ontological neighbourhood

Information engineering

Data science: Dimensionality reduction

Machine learning: Topics in machine learning

Related lectures (33)