In this paper we propose a framework of topic modeling ensembles, a novel solution to combine the models learned by topic modeling over each partition of the whole corpus. It has the potentials for applications such as distributed topic modeling for large corpora, and incremental topic modeling for rapidly growing corpora. Since only the base models, not the original documents, are required in the ensemble, all these applications can be performed in a privacy preserving manner. We explore the theoretical foundation of the proposed framework, give its geometric interpretation, and implement it for both PLSA and LDA. The evaluation of the implementations over the synthetic and real-life data sets shows that the proposed framework is much more efficient than modeling the original corpus directly while achieves comparable effectiveness in terms of perplexity and classification accuracy.
In this paper, a compression method of encoding/decoding 3D mesh based on octree is proposed. Vertices of the 3D mesh are reclassified according to the octree rule. We analyse all the nodes of the octree statistically to identify the type of nodes which accounts for the max proportion and encode them with fewer bits. According to the transmission sequence of geometric information, we rearrange topology and attribute information and encode them. Progressive strategies adopted by the single model and the scene are different in order to maximize the use of network bandwidth and computational performance of local machines. This method has high compression rate, is adapt to network transmission with short response time at the client and can control the level of detail of the model decoding.
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