Stochastic Greedy Algorithms For Multiple Measurement Vectors

Autor: Anna Ma, Jing Qin, Shuang Li, Natalie Durgin, Chenxi Huang, Rachel Grotheer, Deanna Needell
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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