2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/213775Dimensional reduction of high dimensional data can be achieved by keeping only the relevant eigenmodes after principal component analysis. However, differentiating relevant eigenmodes from the random noise eigenmodes is problematic. A new method based on the random matrix theory and a statistical goodness-of-fit test is proposed in this paper. It is validated by numerical simulations and applied to real-time magnetic resonance cardiac cine images.7 pages, 5 figuresData Analysis, Statistics and ProbabilityDisordered Systems and Neural NetworksMedical PhysicsMachine Learningtext