کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6941412 | 1450110 | 2018 | 33 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Low rank matrix completion using truncated nuclear norm and sparse regularizer
ترجمه فارسی عنوان
تکمیل ماتریس پایین با استفاده از تکه های هسته ی کوتاه شده و تنظیم کننده ی ضعیف
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کلمات کلیدی
تکمیل ماتریس، رتبه پایین هسته هسته ی کوتاه شده، نمایندگی انحصاری،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Matrix completion is a challenging problem with a range of real applications. Many existing methods are based on low-rank prior of the underlying matrix. However, this prior may not be sufficient to recover the original matrix from its incomplete observations. In this paper, we propose a novel matrix completion algorithm by employing the low-rank prior and a sparse prior simultaneously. Specifically, the matrix completion task is formulated as a rank minimization problem with a sparse regularizer. The low-rank property is modeled by the truncated nuclear norm to approximate the rank of the matrix, and the sparse regularizer is formulated as an â1-norm term based on a given transform operator. To address the raised optimization problem, a method alternating between two steps is developed, and the problem involved in the second step is converted to several subproblems with closed-form solutions. Experimental results show the effectiveness of the proposed algorithm and its better performance as compared with the state-of-the-art matrix completion algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing: Image Communication - Volume 68, October 2018, Pages 76-87
Journal: Signal Processing: Image Communication - Volume 68, October 2018, Pages 76-87
نویسندگان
Jing Dong, Zhichao Xue, Jian Guan, Zi-Fa Han, Wenwu Wang,