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In this paper, in order to properly evaluate the relative importance of priors and observed data in the Bayesian framework, we propose an extended Gaussian mixture model (EGMM) and design the corresponding learning inference algorithms. First, we define the likelihood function of the EGMM and then propose the variational learning algorithm for this EGMM. Moreover, the proposed model and approach are...
The recent literature indicates that preserving global pairwise sample similarity is of great importance for feature selection and that many existing selection criteria essentially work in this way. In this paper, we argue that besides global pairwise sample similarity, the local geometric structure of data is also critical and that these two factors play different roles in different learning scenarios...
The existing network information security is mainly focused on the classification of gateway or network perimeter and so on. The lack of measures relevant to security threat from intranet hosts leads to information divulging with great loss. The intranet should be of high security. And it's a latent threat when the intranet users visit the website with illegal motives. In view of these, the security...
In this paper through analyzing the characteristics of the history data in bridge safety monitoring system, we explain the importance and features of process data compression. The theory of SDT and improved SDT have been introduced in this paper, analyzing the performances of the algorithm and simulation used by SILAB have been given in this paper. By comparing the performance of the two algorithms,...
A new distributed algorithm of data compression based on hierarchical cluster model for sensor networks is proposed, in which, the whole sensor network is mapped into a kind of hierarchical clusters model firstly, and then different wavelet transform models are used to commit data compression in inner and super clusters respectively. Theoretical analyses and simulation results show that, the above...
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