التفاصيل البيبلوغرافية
| العنوان: |
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations |
| المؤلفون: |
Isaac, Yoann, Barthélemy, Quentin, Gouy-Pailler, Cédric, Sebag, Michèle, Atif, Jamal |
| سنة النشر: |
2016 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Data Structures and Algorithms, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Learning |
| الوصف: |
This paper addresses the structurally-constrained sparse decomposition of multi-dimensional signals onto overcomplete families of vectors, called dictionaries. The contribution of the paper is threefold. Firstly, a generic spatio-temporal regularization term is designed and used together with the standard $\ell_1$ regularization term to enforce a sparse decomposition preserving the spatio-temporal structure of the signal. Secondly, an optimization algorithm based on the split Bregman approach is proposed to handle the associated optimization problem, and its convergence is analyzed. Our well-founded approach yields same accuracy as the other algorithms at the state-of-the-art, with significant gains in terms of convergence speed. Thirdly, the empirical validation of the approach on artificial and real-world problems demonstrates the generality and effectiveness of the method. On artificial problems, the proposed regularization subsumes the Total Variation minimization and recovers the expected decomposition. On the real-world problem of electro-encephalography brainwave decomposition, the approach outperforms similar approaches in terms of P300 evoked potentials detection, using structured spatial priors to guide the decomposition. |
| نوع الوثيقة: |
Working Paper |
| URL الوصول: |
http://arxiv.org/abs/1609.09525 |
| رقم الانضمام: |
edsarx.1609.09525 |
| قاعدة البيانات: |
arXiv |