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31.
This paper aims to study the deep clustering problem with heterogeneous features and unknown cluster number. To address this issue, a novel deep Bayesian clustering framework is proposed. In particular, a heterogeneous feature metric is first constructed to measure the similarity between different types of features. Then, a feature metric-restricted hierarchical sample generation process is established, in which sample with heterogeneous features is clustered by generating it from a similarity constraint hidden space. When estimating the model parameters and posterior probability, the corresponding variational inference algorithm is derived and implemented. To verify our model capability, we demonstrate our model on the synthetic dataset and show the superiority of the proposed method on some real datasets. Our source code is released on the website: Github.com/yexlwh/Heterogeneousclustering. 相似文献
32.
We describe an implementation of a parallel document clustering scheme based on latent semantic indexing, which uses singular value decomposition. Given a set of documents, the clustering algorithm is dynamic in the sense that it automatically infers the number of clusters to be output. The parallel version has been implemented on a LAN and on a dual‐core system. Experimental evaluation of the algorithm shows an average speed‐up of 6.22 for the LAN implementation and an average speed‐up of 3.71 for the dual‐core implementation, while still maintaining a precision and recall in the range [0.85, 1]. To put these implementations in the context of information retrieval, we use the parallel clustering algorithm and develop a document similarity search system. The similarity search system shows good performance in terms of precision and recall. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
33.
This paper describes a cluster-based method for combining differently exposed images in order to increase their dynamic range. Initially an image is decomposed into a set of arbitrary shaped regions. For each region we compute a utility function which is based on the amount of presented information and an entropy. This function is used to select the most appropriate exposure for each region. After the exposures are selected, a bilateral filtering is applied in order to make the interregional transitions smooth. As a result we obtain weighting coefficients for each exposure and pixel. An output image is combined from clusters of input images using weights. Each pixel of the output image is calculated as a weighted sum of exposures. The proposed method allows recovering details from overexposed and underexposed parts of image without producing additional noise. Our experiments show effectiveness of the algorithm for the high dynamic range scenes. It requires no information about shutter speed or camera parameters. This method shows robust results even if the exposure difference between input images is 2-stops or higher. 相似文献
34.
针对上下位关系在分类层级结构建立阶段遇到的多义性问题,给出一种概念空间中上下位关系意义识别的方法.单个概念的意义识别问题被转换为概念空间中上下位关系的意义识别.首先利用并列语境解决语境稀疏问题,获取上下位关系意义的语境.然后利用<同义词词林>对每个语境进行词义修正,以三种特征计算特征词权重,构建"关系一词'的高维向量空间,然后通过潜在语义分析降维,获取上下位关系意义的潜在语义,最后组平均聚类后得到关系的意义划分.在实验中,给出了聚类阈值自动调整函数,分析了词林和潜在语义分析的作用,实验结果证实了方法的有效性. 相似文献
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