On principal component analysis of the convex combination of two data matrices and its application to acoustic metamaterial filters
This provides theoretical analysis for a specific matrix perturbation scenario, with potential applications in engineering domains like acoustic metamaterials.
The paper investigates eigenvalue perturbation bounds for principal component analysis when applied to convex combinations of two data matrices, and discusses applications to multi-objective optimization problems such as acoustic metamaterial filter design.
In this short paper, a matrix perturbation bound on the eigenvalues found by principal component analysis is investigated, for the case in which the data matrix on which principal component analysis is performed is a convex combination of two data matrices. The application of the theoretical analysis to multi-objective optimization problems (e.g., those arising in the design of acoustic metamaterial filters) is briefly discussed, together with possible extensions.