一种多视图深度融合的连续性缺失补全方法
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摘要
针对现有连续性缺失补全方法的不足,建立了一种多视图深度融合的连续性缺失补全方法。该方法采用反转距离加权插值、双向简单指数平滑、用户协同过滤、能量扩散协同过滤及文本嵌套的方法,分别得到时空和语义缺失数据补全中间结果;构造了神经网络模型融合跨时空和语义视图中的互补异构信息,完成连续性缺失补全。实验表明,该方法补全连续性缺失不但效率高,而且比时空多视图补全在平均绝对误差与平均相对误差上分别降低7%和22%,具备普适性且适用于相关时空连续性缺失序列补全领域。
Aiming at the shortcomings of existing successive missing complement methods,a successive missing data completion method for multi-view depth fusion is established.The method adopts inverse distance weighted interpolation,bidirectional simple exponential smoothing,user-based collaborative filtering,the collaborative filtering based on mass diffusion and structure embeddings,to obtain intermediate results of five missing data in spatiotemporal and semantic respectively;then,this method constructs a neural network model that combines complementary heterogeneous information across time and space and semantic views to achieve successive missing completion.Experimental results show that the method is universally applicable to the field of Spatial-Temporal successive missing sequence completion and,that it not only achieves a high efficiency,but also reduces the mean absolute error and the mean relative error by 7% and 22%,respectively,compared with the Spatial-Temporal Multi-view completion method.
Aiming at the shortcomings of existing successive missing complement methods,a successive missing data completion method for multi-view depth fusion is established.The method adopts inverse distance weighted interpolation,bidirectional simple exponential smoothing,user-based collaborative filtering,the collaborative filtering based on mass diffusion and structure embeddings,to obtain intermediate results of five missing data in spatiotemporal and semantic respectively;then,this method constructs a neural network model that combines complementary heterogeneous information across time and space and semantic views to achieve successive missing completion.Experimental results show that the method is universally applicable to the field of Spatial-Temporal successive missing sequence completion and,that it not only achieves a high efficiency,but also reduces the mean absolute error and the mean relative error by 7% and 22%,respectively,compared with the Spatial-Temporal Multi-view completion method.
引文
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[9]NIE D C,AN Y H,DONG Q,et al.Information Filtering via Balanced Diffusion on Bipartite Networks[J].Physica A:Statistical Mechanics and Its Applications,2015,421:44-53.
[10]QIU X,MAO Q,TANG Y,et al.Reversed Graph Embedding Resolves Complex Single-cell Trajectories[J].Nature Methods,2017,14(10):979-982.
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[12]LIN G,SHEN W.Research on Convolutional Neural Network Based on Improved Relu Piecewise Activation Function[J].Procedia Computer Science,2018,131:977-984.
[13]SZEGEDY C,IOFFE S,VANHOUCKE V,et al.Inception-v4,Inception-resnet and the Impact of Residual Connections on Learning[C]//Proceedings of the 2017 31st AAAI Conference on Artificial Intelligence.Palo Alto:AAAI press,2017:4278-4284.
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