多方面流式张量补全Multi-Aspect Streaming Tensor Completion |
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课程网址: | http://videolectures.net/kdd2017_song_streaming_tensor/ |
主讲教师: | 宋清泉 |
开课单位: | 德州农工大学 |
开课时间: | 2017-10-09 |
课程语种: | 英语 |
中文简介: | 张量补全已成为许多现实世界数据驱动应用中的有效计算工具。除了传统的静态设置之外,随着高速流数据的日益普及,需要高效的在线处理,而无需从头开始重建整个模型。流式张量完成的现有工作通常建立在张量仅以一种模式增长的假设之上。不幸的是,该假设在许多现实情况下并不成立,在这些情况下,张量可能以多种模式增长,即多方面流张量。由于张量模式变化的不确定性和多方面流张量的复杂数据结构,在不牺牲其有效性的情况下有效建模和完成这些增量张量仍然是一项具有挑战性的任务。为了弥补这一差距,我们提出了一种基于 CANDECOMP/PARAFAC (CP) 分解的多方面流张量补全框架 (MAST),以跟踪一般增量张量的子空间以完成补全。此外,我们研究了时间是张量的一种模式的特殊情况,并利用其额外的结构信息来改进总体框架以获得更高的效率。从各种实际应用中收集的四个数据集的实验结果证明了所提出的框架的有效性和效率。并利用其额外的结构信息来改进总体框架以提高效率。从各种实际应用中收集的四个数据集的实验结果证明了所提出的框架的有效性和效率。并利用其额外的结构信息来改进总体框架以提高效率。从各种实际应用中收集的四个数据集的实验结果证明了所提出的框架的有效性和效率。 |
课程简介: | Tensor completion has become an effective computational tool in many real-world data-driven applications. Beyond traditional static setting, with the increasing popularity of high velocity streaming data, it requires efficient online processing without reconstructing the whole model from scratch. Existing work on streaming tensor completion is usually built upon the assumption that tensors only grow in one mode. Unfortunately, the assumption does not hold in many real-world situations in which tensors may grow in multiple modes, i.e., multi-aspect streaming tensors. Efficiently modeling and completing these incremental tensors without sacrificing its effectiveness remains a challenging task due to the uncertainty of tensor mode changes and complex data structure of multi-aspect streaming tensors. To bridge this gap, we propose a Multi-Aspect Streaming Tensor completion framework (MAST) based on CANDECOMP/PARAFAC (CP) decomposition to track the subspace of general incremental tensors for completion. In addition, we investigate a special situation where time is one mode of the tensors, and leverage its extra structure information to improve the general framework towards higher effectiveness. Experimental results on four datasets collected from various real-world applications demonstrate the effectiveness and efficiency of the proposed framework. |
关 键 词: | 张量补全; 高速流数据; 数据科学 |
课程来源: | 视频讲座网 |
数据采集: | 2023-12-27:wujk |
最后编审: | 2023-12-27:wujk |
阅读次数: | 30 |