کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
562839 1451946 2016 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Alternating strategies with internal ADMM for low-rank matrix reconstruction
ترجمه فارسی عنوان
استراتژی های متناوب با ADMM داخلی برای بازسازی ماتریس پایین رتبه
کلمات کلیدی
بازسازی ماتریس پایین رتبه ؛ استراتژی های متناوب؛ کمترین مربعات؛ ADMM
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی


• Alternating optimization strategies are good for recovering matrices.
• Matrices in consideration are low-rank matrices with linear parameterized structures.
• The algorithm combines an alternating least-squares based strategy with ideas from ADMM.
• Merging these two strategies leads to a better performance and less consumed time.

This paper focuses on the problem of reconstructing low-rank matrices from underdetermined measurements using alternating optimization strategies. We endeavour to combine an alternating least-squares based estimation strategy with ideas from the alternating direction method of multipliers (ADMM) to recover low-rank matrices with linear parameterized structures, such as Hankel matrices. The use of ADMM helps to improve the estimate in each iteration due to its capability of incorporating information about the direction of estimates achieved in previous iterations. We show that merging these two alternating strategies leads to a better performance and less consumed time than the existing alternating least squares (ALS) strategy. The improved performance is verified via numerical simulations with varying sampling rates and real applications.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 121, April 2016, Pages 153–159
نویسندگان
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