http://arxiv.org/abs/2010.10375
In order to handle the large data sets omnipresent in modern science, efficient compression algorithms are necessary. There exist general purpose lossless and lossy compression algorithms, suited for different situations. Here, a Bayesian data compression (BDC) algorithm that adapts to the specific data set is derived. BDC compresses a data set under conservation of its posterior structure with minimal information loss given the prior knowledge on the signal, the quantity of interest. BDC works hand in hand with the signal reconstruction from the data. Its basic form is valid for Gaussian priors and likelihoods. This generalizes to non-linear settings with the help of Metric Gaussian Variational Inference. BDC requires the storage of effective instrument response functions for the compressed data and corresponding noise encoding the posterior covariance structure. Their memory demand counteract the compression gain. In order to improve this, sparsity of the compressed responses can be enforced by separating the data into patches and compressing them separately. The applicability of our method is demonstrated by applying it to synthetic data and radio astronomical data. Still the algorithm needs to be improved further as the computation time of the compression exceeds the time of the inference with the original data.
J. Harth-Kitzerow, R. Leike, P. Arras, et. al.
Wed, 21 Oct 20
37/79
Comments: 31 pages, 15 figures, 1 table, for code, see this https URL