Fair Canonical Correlation Analysis.
Adv Neural Inf Process Syst
; 36: 3675-3705, 2023 Dec.
Article
em En
| MEDLINE
| ID: mdl-38665178
ABSTRACT
This paper investigates fairness and bias in Canonical Correlation Analysis (CCA), a widely used statistical technique for examining the relationship between two sets of variables. We present a framework that alleviates unfairness by minimizing the correlation disparity error associated with protected attributes. Our approach enables CCA to learn global projection matrices from all data points while ensuring that these matrices yield comparable correlation levels to group-specific projection matrices. Experimental evaluation on both synthetic and real-world datasets demonstrates the efficacy of our method in reducing correlation disparity error without compromising CCA accuracy.
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Base de dados:
MEDLINE
Idioma:
En
Revista:
Adv Neural Inf Process Syst
Ano de publicação:
2023
Tipo de documento:
Article