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Expectation-Maximization (EM) is typically used to compute maximum likelihood estimates given incomplete samples and estimated the parameters. We proposed a new algorithm for generating an extension Dynamic Topic Model (exDTM)-in a time-based manner and based on the distribution of documents topics on Spark. The proposed algorithm can be applied in clustering documents from data streams for threat...
We propose a novel approach for the crowd anomaly detection in multiple cameras with non-overlapping view. In this paper, we refer to the activities of crowd in far-field scenes. Firstly, we present a model for learning all of the motion patterns under single camera view, which are regarded as the normal situation. In the surveillance region, we mark the entrances and exits under the single camera...
The use of clustering systems is very important in those real-word applications where an efficient, both accurate and economical, representation of the data to be processed is necessary. When dealing with statistical models, such a problem is usually related to the estimate of their parameters in the Maximum Likelihood context. At this regard, we propose an EM-based algorithm that uses a hierarchical...
By utilizing the must-link or cannot-link pair wise constraints in data, semi-supervised clustering improves the performance of unsupervised clustering significantly. A number of semi-supervised clustering algorithms have been proposed to consider such pair wise constraints. However, most of them assign a hard label to each data item and produce little information about the cluster itself. In this...
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