Low-Rank Representation for Incomplete Data
- Release time:2024-08-09
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Affiliation of Author(s):
理学院
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Journal:
Mathematical Problems in Engineering
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Key Words:
中文关键字：低秩矩阵恢复，英文关键字：Low-rank matrix recovery
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Abstract:
Low-rank matrix recovery (LRMR) has been becoming an increasingly popular technique for analyzing data with missing entries,
grosscorruptions,andoutliers.AsasignificantcomponentofLRMR,themodeloflow-rankrepresentation(LRR)seeksthelowest-
rankrepresentationamongallsamples anditisrobustforrecovering subspacestructures.Thispaper attemptstosolvetheproblem
ofLRRwithpartiallyobservedentries.Firstly,weconstructanonconvexminimizationbytakingthelowrankness,robustness,and
incompletionintoconsideration.ThenweemploythetechniqueofaugmentedLagrangemultiplierstosolvetheproposedprogram.
Finally, experimental results on synthetic and real-world datasets validate the feasibility and effectiveness of the proposed method.
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Note:
SCI
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Co-author:
雍龙泉
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First Author:
zhengxiuyun,yangwei,shijiarong
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Indexed by:
Journal paper
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Volume:
卷：2014
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Issue:
期：
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Page Number:
页：
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Translation or Not:
no
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Date of Publication:
2014-12-01
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