# On principal component analysis of the convex combination of two data matrices and its application to acoustic metamaterial filters

arXiv: https://arxiv.org/abs/2104.07054 · HTML: https://mlsanity.com/paper/2104.07054/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 4 Apr 2021.

## Builds on (most cited first, 1 of 1)

- [Machine-learning techniques for the optimal design of acoustic metamaterials](https://mlsanity.com/paper/1908.10645/) (2019)

## Abstract (arXiv)

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.