Lecture

Principal Component Analysis: Applications and Limitations

Description

This lecture covers the application of Principal Component Analysis (PCA) in reconstructing generative models, denoising, compression, and regression. It explores the limitations of PCA in preserving local neighborhood relationships and the importance of scaling data. The lecture also discusses alternative techniques like t-SNE for dimensionality reduction.

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Official source

https://mediaspace.epfl.ch/media/0_uwgud4fm

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Data analysis: Regression analysis

Information engineering

Data science: Dimensionality reduction, Topics in data science

Signal processing: Data compression

Mathematics

Algebra: Linear algebra

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