{'fraction_non_alphanumeric': 0.016506189821182942, 'fraction_numerical': 0.002751031636863824, 'mean_word_length': 5.867924528301887, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Novel Compressive Sensing Scheme under the Variational Bayesian Framework

In this work we provide a novel algorithm for Bayesian Compressive Sensing. The proposed algorithm is considered for signals that features two properties: grouping structure and sparsity between groups. The Compressive Sensing problem is formulated using the Bayesian linear model. Furthermore, the sparsity of the unknown signal is modeled by a parameterized sparse prior while the inference procedure is conducted using the Variational Bayesian framework. Experimental results, using 1D and 2D signals, demonstrate that the proposed algorithm provides superior performance compared to state-of-the-art Compressive Sensing reconstruction algorithms.