{'fraction_non_alphanumeric': 0.022878932316491896, 'fraction_numerical': 0.0, 'mean_word_length': 4.801104972375691, '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}}
Unsupervised Bayesian Fusion of Correlated Sensors

We address in this paper the problem of classifying of multidimensional data. We adopt the context of Bayesian unsupervised classification, so that our problem amounts to estimating a mixture of k components on R m , where k is the number of classes and m is the number of sensors. When these components are Gaussian, one can use some general methods like Expectation-Maximization (EM) or Iterative Conditional Estimation (ICE). When the components are not Gaussian but the components of each of them are independent, one can still estimate such a mixture by the use of ICE or some stochastic variant of EM. We attack in this paper the more general problem of possibly correlated and non Gaussian sensors. A new method, called ICE-COR, of estimation of the corresponding mixture is presented and we provide some simulation results. The method proposed is inspired from a recent "generalized" mixture estimates, which means that we do not know, a priori, what the exact forms of the components are.