Wavelet sparse principal components for distributed data.

• Grant number: 25/21250-2
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: October 01, 2025
• End date: September 30, 2027
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Statistics
• Principal Investigator: Aluísio de Souza Pinheiro
• Grantee: Giovanni Barbosa Herrero
• Host Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 23/02538-0 - Time series, wavelets, high dimensional data and applications, AP.TEM

Abstract

Both the potentially great volume of data or its sensitive nature may hinder its free exchange. This leads to the so-called distributed data, which is also know as federated learning. In this setup subsamples are distributed among different processors, and statistical problems must be solved in a distributed/federated manner without a shared full access to the whole data. Whenever one is concerned with dimensionality reduction, principle components analysis (PCA) is a common option. We seek assessing wavelet PCA's performance in this federated data scenario. (AU)