L1 Regularization: Sparse Solutions and Dimensionality Reduction

In course

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Description

This lecture covers L1 regularization, focusing on the concept of replacing empirical risk with a regularized version. It explores how L1 regularization leads to sparse solutions by relying on a few significant entries from the observation vector. The lecture also discusses the concept of dimensionality reduction and the benefits of using elastic-net regularization. Various mathematical derivations and interpretations related to Laplacian priors and optimization problems are presented.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_4aa65kzv

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Ontological neighbourhood

Mathematics

Analysis: Numerical analysis

Statistics

Data analysis: Cross-validation

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