Review and comparison of dimensionality reduction techniques.
Authorship
A.A.M.
Bachelor of Mathematics
A.A.M.
Bachelor of Mathematics
Defense date
07.16.2025 11:45
07.16.2025 11:45
Summary
Dimensionality reduction techniques are fundamental procedures in statistics for simplifying datasets while losing the least amount of information as possible. The objective of this work is to thoroughly review some of these methods, with a particular focus on one of the most widely used techniques: Principal Component Analysis. Additionally, we will discuss other more recent nonlinear techniques, which have been gaining popularity in recent years. Finally, to emphasize the practical importance of these techniques, we will present application examples with real datasets to explore the challenges of interpretation and processing they entail.
Dimensionality reduction techniques are fundamental procedures in statistics for simplifying datasets while losing the least amount of information as possible. The objective of this work is to thoroughly review some of these methods, with a particular focus on one of the most widely used techniques: Principal Component Analysis. Additionally, we will discuss other more recent nonlinear techniques, which have been gaining popularity in recent years. Finally, to emphasize the practical importance of these techniques, we will present application examples with real datasets to explore the challenges of interpretation and processing they entail.
Direction
PATEIRO LOPEZ, BEATRIZ (Tutorships)
PATEIRO LOPEZ, BEATRIZ (Tutorships)
Court
CRUJEIRAS CASAIS, ROSA MARÍA (Chairman)
PENA BRAGE, FRANCISCO JOSE (Secretary)
DOMINGUEZ VAZQUEZ, MIGUEL (Member)
CRUJEIRAS CASAIS, ROSA MARÍA (Chairman)
PENA BRAGE, FRANCISCO JOSE (Secretary)
DOMINGUEZ VAZQUEZ, MIGUEL (Member)