4.7 Article

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

期刊

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2020.3015914

关键词

Feature extraction; Multiplexing; Optimization; Pattern recognition; Sensors; Signal to noise ratio; Engines; Information fusion; feature level; random matrix theory; parameter estimation; signal matrices

资金

  1. National Natural Science Foundation of China [71871233]
  2. Fundamental Research Funds for the Central Universities of China [2020XDA01-1]
  3. Beijing Natural Science Foundation [9182015, 1202020]

向作者/读者索取更多资源

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.
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.

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