- Friday 22 Apr 2022, 12:00 - 13:00
- Spoken Language
- Room 1-13
- Sanders Building
Online (via Zoom)
PCA is a popular tool for exploratory data analysis and dimension reduction, especially in the high-dimensional setting. To improve interpretability, several PCA methods generating sparse solutions have been proposed.
Solving the sparse SCA problem is intractable, given its combinatorial nature. In this seminar, different sparse PCA methods are analysed, focusing on the optimisation criteria used to achieve sparseness.
Practical issues are discussed, such as the misconception that equivalent PCA formulations remain equivalent under sparsity. Finally, an extension to the problem of sparse Simultaneous Component Analysis is presented.
About Rosember Guerra Urzola
Rosember Guerra-Urzola is a last-year PhD student at Tilburg University. His PhD research has focused on the sparse PCA problem from the statistical and machine learning viewpoint and its applications to the social sciences.
- Michal Mankowski
- Olga Kuryatnikova