2010/08/21 by Hadi Zayyani, Zayyani, Hadi, Massoud Babaie‐Zadeh +4
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Bayesian inference #Bayesian probability #Blind Source Separation Techniques #Computer science #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine learning #Mathematics #Representation (politics) #Sequence (biology) #Sparse and Compressive Sensing Techniques #Sparse approximation #Statistical hypothesis testing #Statistics #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1008.3618
arxiv created 2010/08/21 · openalex publication_date 2010/08/21 · arxiv updated 2010/08/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we propose a Bayesian Hypothesis Testing Algorithm (BHTA) for sparse representation. It uses the Bayesian framework to determine active atoms in sparse representation of a signal. The Bayesian hypothesis testing based on three assumptions, determines the active atoms from the correlations and leads to the activity measure as proposed in Iterative Detection Estimation (IDE) algorithm. In fact, IDE uses an arbitrary decreasing sequence of thresholds while the proposed algorithm is based on a sequence which derived from hypothesis testing. So, Bayesian hypothesis testing framework leads to an improved version of the IDE algorithm. The simulations show that Hard-version of our suggested algorithm achieves one of the best results in terms of estimation accuracy among the algorithms which have been implemented in our simulations, while it has the greatest complexity in terms of simulation time.