Exploring bounded component analysis using an l_∞ norm criterion

Autor(es)

Brotto, Renan D. B. ; Nose-Filho, Kenji ; Romano, Joao M. T.

• Tipo de documento: Artigo Científico
• Fonte: DIGITAL SIGNAL PROCESSING; v. 154, p. 9-pg., 2024-07-29.

Resumo

In this paper we propose a new criterion for the Blind Source Separation (BSS) of antisparse bounded sources, based on the sum of the 8 infinity-norm infinity-norm of the sources. Based on the observation that the mixing process of bounded sources with any mixing matrix with unitary Frobenius norm will increase the 8 infinity-norm infinity-norm of the sources, unless it is the identity matrix, the minimization of the sum of the 8 infinity-norm infinity-norm of the sources can be used for the estimation of a separation matrix. To that, a Principle Component Analysis technique followed by a Givens Rotations based optimization method can be used for the separation of independent bounded sources. Also, the Givens Rotations based optimization method can be used for the separation of correlated bounded sources mixed by a rotation matrix. We theoretically analyze the proposed criterion and assess its performance through numerical simulations involving three distinct types of bounded signals. Our theoretical and experimental findings underscore the efficacy of the 8 infinity infinity norm as a suitable contrast function for antisparse bounded sources, showcasing its superior performance relative to a state-of-the-art algorithm. (AU)

• Processo FAPESP: 17/13025-2 - Sobre o uso de critérios Lp nos problemas de desconvolução não-supervisionada e separação cega de fontes
• Beneficiário: Renan Del Buono Brotto
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 20/09838-0 - BI0S - Brazilian Institute of Data Science
• Beneficiário: João Marcos Travassos Romano
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia
• Processo FAPESP: 19/20899-4 - Antiesparsidade e equidade em processamento de sinais: da separação cega de fontes ao aprendizado de máquina equânime
• Beneficiário: Renan Del Buono Brotto
• Modalidade de apoio: Bolsas no Brasil - Doutorado