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

• Published in: IEEE transactions on knowledge and data engineering Vol. 34; no. 6; pp. 2860 - 2871
• Main Authors: Li, Hui-Jia, Wang, Zhen, Cao, Jie, Pei, Jian, Shi, Yong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Data integration; Eigenvectors; Engines; Feature extraction; feature level; Information fusion; Mathematical analysis; Matrix theory; Multiplexing; Noise levels; Optimization; parameter estimation; Pattern recognition; random matrix theory; Sensors; signal matrices; Signal to noise ratio

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 obj...