{'fraction_non_alphanumeric': 0.019933554817275746, 'fraction_numerical': 0.0008305647840531562, 'mean_word_length': 5.694444444444445, '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}}
Sparse Markov net learning with priors on regularization parameters

In this paper, we consider the problem of structure recovery in Markov Network over Gaussian variables, that is equivalent to finding the zero-pattern of the sparse inverse covariance matrix. Recently proposed l1-regularized optimization methods result into convex problems that can be solved optimally and efficiently. However, the accuracy such methods can be quite sensitive to the choice of regularization parameter, and optimal selection of this parameter remains an open problem. Herein, we adopt a Bayesian approach, treating the regularization parameter(s) as random variable(s) with some prior, and using MAP optimization to find both the inverse covariance matrix and the unknown regularization parameters. Our general formulation allows a vector of regularization parameters and is well-suited for learning structured graphs such as scale-free networks where the sparsity of nodes varies significantly. We present promising empirical results on both synthetic and real-life datasets, demonstrating that our approach achieves a better balance between the false-positive and falsenegative errors than commonly used approaches.