Rosember Guerra Urzola

rosemberSchool of Social and Behavioral Sciences
Methodology and Statistics
Tilburg University

On 1 November 2023 Rosember Guerra Urzola defended his thesis: Jack of all trades, Master of None: The trade-offs in sparse PCA methods for diverse purposes at the university of Tilburg.

Sparse algorithms are becoming increasingly popular in data science research because they can identify and select the most relevant variables in a dataset while minimizing overfitting. However, sparse algorithms present unique challenges when dealing with social data, such as data integration (heterogeneity) and the need to account for complex social interactions and dynamics. Throughout this thesis, I focused on researching the sparse Principal Component Analysis (sPCA) problem. I have explored and developed sPCA algorithms that can effectively identify and select the essential features in a dataset, reducing its dimensionality or underlying factors in the data. Specifically, I examined sPCA methods that utilize sparsity-inducing penalties and cardinality constraints to achieve sparsity in the solution.

Prof. dr. K. Sijtsma, dr. K.  van Deun, dr. J.C. Vera Lizcano

Financed by
Data Science Tilburg University

September 2018 – November 2023