Optimal Estimation of Low-Rank Factors via Feature Level Data Fusion of Multiplex Signal Systems

The design of fusion engines is a subject of great importance in a variety of fields. In this paper, we focus on the problem of linear fusion at the feature level for multiple signal matrices with noises, with the features being extremal eigenvectors. When given multiple similarity matrices, the objective is to find an estimate of the latent signal eigenspace. The concentration result for the inner product of features from different matrix samples is developed, utilizing the random matrix theory. Based on of the theoretical results, we proposed an efficient algorithm, EigFuse, to solve the constrained data-driven optimization problem with different level of noises. Our method is of high efficiency by comparing it with state-of-the-art baseline approaches with multiple noise levels. Comprehensive experiments on several synthetic as well as real-life networks demonstrate our method's superior performance.

Automated summary

This paper discusses the significance of fusion engine design in various fields. The focus is on the problem of linear fusion at the feature level for multiple signal matrices with noises, and an efficient algorithm is proposed to solve this problem. The experimental results verify the superior performance of the algorithm in synthetic and real-life networks.