Stochastic Greedy Algorithms For Multiple Measurement Vectors
Autor: | Anna Ma, Jing Qin, Shuang Li, Natalie Durgin, Chenxi Huang, Rachel Grotheer, Deanna Needell |
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Jazyk: | angličtina |
Rok vydání: | 2017 |
Předmět: |
FOS: Computer and information sciences
Control and Optimization Computer science 02 engineering and technology 01 natural sciences Convergence (routing) Computer Science - Data Structures and Algorithms 0202 electrical engineering electronic engineering information engineering FOS: Mathematics Discrete Mathematics and Combinatorics 68W20 94A12 47N10 Data Structures and Algorithms (cs.DS) Mathematics - Numerical Analysis 0101 mathematics Greedy algorithm Mathematics - Optimization and Control Computer Science::Information Theory Regular polygon Sparse approximation Numerical Analysis (math.NA) Matching pursuit Thresholding 010101 applied mathematics Compressed sensing Optimization and Control (math.OC) Modeling and Simulation 020201 artificial intelligence & image processing Stochastic optimization Algorithm Analysis |
Popis: | Sparse representation of a single measurement vector (SMV) has been explored in a variety of compressive sensing applications. Recently, SMV models have been extended to solve multiple measurement vectors (MMV) problems, where the underlying signal is assumed to have joint sparse structures. To circumvent the NP-hardness of the \begin{document}$ \ell_0 $\end{document} minimization problem, many deterministic MMV algorithms solve the convex relaxed models with limited efficiency. In this paper, we develop stochastic greedy algorithms for solving the joint sparse MMV reconstruction problem. In particular, we propose the MMV Stochastic Iterative Hard Thresholding (MStoIHT) and MMV Stochastic Gradient Matching Pursuit (MStoGradMP) algorithms, and we also utilize the mini-batching technique to further improve their performance. Convergence analysis indicates that the proposed algorithms are able to converge faster than their SMV counterparts, i.e., concatenated StoIHT and StoGradMP, under certain conditions. Numerical experiments have illustrated the superior effectiveness of the proposed algorithms over their SMV counterparts. |
Databáze: | OpenAIRE |
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