文章基本信息

标题：On Low-Rank Hankel Matrix Denoising
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作者：Mingzhou Yin ; Roy S. Smith
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：7
页码：198-203
DOI：10.1016/j.ifacol.2021.08.358
语种：English
出版社：Elsevier
摘要：AbstractThe low-complexity assumption in linear systems can often be expressed as rank deficiency in data matrices with generalized Hankel structure. This makes it possible to denoise the data by estimating the underlying structured low-rank matrix. However, standard low-rank approximation approaches are not guaranteed to perform well in estimating the noise-free matrix. In this paper, recent results in matrix denoising by singular value shrinkage are reviewed. A novel approach is proposed to solve the low-rank Hankel matrix denoising problem by using an iterative algorithm in structured low-rank approximation modified with data-driven singular value shrinkage. It is shown numerically in both the input-output trajectory denoising and the impulse response denoising problems, that the proposed method performs the best in terms of estimating the noise-free matrix among existing algorithms of low-rank matrix approximation and denoising.
关键词：KeywordsMatrix denoisingHankel matrixlow-rank approximationsubspace methodsdata-driven modelling