{'fraction_non_alphanumeric': 0.010526315789473684, 'fraction_numerical': 0.0, 'mean_word_length': 5.594059405940594, '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}}
Volumetric Data Modeling and Reduction in a Compressive Sensing Framework

We propose an alternative volumetric data modeling and reduction approach via compressive sensing theory. We provide evidence that with a small set of randomly chosen Fourier samples of a dataset, it is possible to recover the dataset accurately. Our experiments demonstrate that the number of samples necessary for an accurate reconstruction is linearly proportional to the number of features, as opposed to the original resolution of the dataset. These experiment results motivate further research in the direction of customdesigned sparse representations for large-scale volumetric data.