Dynamic Matrix Recovery

[2305.10524] Dynamic Matrix Recovery Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Methodology arXiv:2305.10524 (stat) [Submitted on 17 May 2023 ( v1 ), last revised 10 Apr 2026 (this version, v3)] Title: Dynamic Matrix Recovery Authors: Ziyuan Chen , Ying Yang , Fang Yao View a PDF of the paper titled Dynamic Matrix Recovery, by Ziyuan Chen and 1 other authors View PDF HTML (experimental) Abstract: Matrix recovery from sparse observations is an extensively studied topic emerging in various applications, such as recommendation system and signal processing, which includes the matrix completion and compressed sensing models as special cases. In this work we propose a general framework for dynamic matrix recovery of low-rank matrices that evolve smoothly over time. We start from the setting that the observations are independent across time, then extend to the setting that both the design matrix and noise possess certain temporal correlation via modified concentration inequalities. By pooling neighboring observations, we obtain sharp estimation error bounds of both settings, showing the influence of the underlying smoothness, the dependence and effective samples. We propose a dynamic fast iterative shrinkage thresholding algorithm that is computationally efficient, and characterize the interplay between algorithmic and statistical convergence. Simulated and real data examples are provided to support such findings. Comments: Journal of the American Statistical Association (2023) Subjects: Methodology (stat.ME) Cite as: arXiv:2305.10524 [stat.ME] (or arXiv:2305.10524v3 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2305.10524 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1080/01621459.2023.2297468 Focus to learn more DOI(s) linking to related resources Submission history From: Ziyuan Chen [ view email ] [v1] Wed, 17 May 2023 19:14:57 UTC (2,976 KB) [v2] Tue, 21 Nov 2023 17:12:05 UTC (3,412 KB) [v3] Fri, 10 Apr 2026 06:31:17 UTC (3,310 KB) Full-text links: Access Paper: View a PDF of the paper titled Dynamic Matrix Recovery, by Ziyuan Chen and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.ME < prev | next > new | recent | 2023-05 Change to browse by: stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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