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Analysis Sparse Representation for Nonnegative Signals Based on Determinant Measure by DC Programming
المؤلفون المشاركون
Li, Yujie
Tan, Benying
Kanemura, Atsunori
Chen, Wuhui
Ding, Shuxue
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-04-24
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Analysis sparse representation has recently emerged as an alternative approach to the synthesis sparse model.
Most existing algorithms typically employ the l0-norm, which is generally NP-hard.
Other existing algorithms employ the l1-norm to relax the l0-norm, which sometimes cannot promote adequate sparsity.
Most of these existing algorithms focus on general signals and are not suitable for nonnegative signals.
However, many signals are necessarily nonnegative such as spectral data.
In this paper, we present a novel and efficient analysis dictionary learning algorithm for nonnegative signals with the determinant-type sparsity measure which is convex and differentiable.
The analysis sparse representation can be cast in three subproblems, sparse coding, dictionary update, and signal update, because the determinant-type sparsity measure would result in a complex nonconvex optimization problem, which cannot be easily solved by standard convex optimization methods.
Therefore, in the proposed algorithms, we use a difference of convex (DC) programming scheme for solving the nonconvex problem.
According to our theoretical analysis and simulation study, the main advantage of the proposed algorithm is its greater dictionary learning efficiency, particularly compared with state-of-the-art algorithms.
In addition, our proposed algorithm performs well in image denoising.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Li, Yujie& Tan, Benying& Kanemura, Atsunori& Ding, Shuxue& Chen, Wuhui. 2018. Analysis Sparse Representation for Nonnegative Signals Based on Determinant Measure by DC Programming. Complexity،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1133321
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Li, Yujie…[et al.]. Analysis Sparse Representation for Nonnegative Signals Based on Determinant Measure by DC Programming. Complexity No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1133321
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Li, Yujie& Tan, Benying& Kanemura, Atsunori& Ding, Shuxue& Chen, Wuhui. Analysis Sparse Representation for Nonnegative Signals Based on Determinant Measure by DC Programming. Complexity. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1133321
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1133321
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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