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 obj...

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Published inIEEE 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
LanguageEnglish
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
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Abstract 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.
AbstractList 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.
Author Cao, Jie
Li, Hui-Jia
Shi, Yong
Wang, Zhen
Pei, Jian
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Snippet 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...
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SubjectTerms 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
Title Optimal Estimation of Low-Rank Factors via Feature Level Data Fusion of Multiplex Signal Systems
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https://www.proquest.com/docview/2656879476
Volume 34
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