Event Analytics via Discriminant Tensor Factorization
Event Analytics via Discriminant Tensor Factorization
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DOI：
10.1145/3184455
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Xidao Wen;Y. Lin;K. Pelechrinis
中科院分区：
文献类型：
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作者：
Xidao Wen;Y. Lin;K. Pelechrinis
分析灾难性事件的影响是理解和应对危机的核心。传统上，对灾害影响的评估主要依靠手工收集和分析调查和调查表以及审查当局的报告。这可能既昂贵又耗时，而及时评估事件的影响对于危机管理和人道主义行动至关重要。在这项工作中，由于移动性数据的多维性，我们将影响发现定义为通过张量分解识别共享和判别子空间的问题。现有的挖掘共享子空间和判别子空间的工作通常需要其中任何一种类型的预定义数量。在事件影响发现的背景下，这可能是不切实际的，特别是对于那些前所未有的事件。为了克服这个问题，我们提出了一个名为“PairFac”的新框架，该框架将多维数据联合分解，以发现潜在的流动性模式及其相关的判别权重。该框架不需要预先分割共享和判别子空间，同时可以从多维行为数据中自动捕获持久和变化的模式。我们的工作在危机管理和城市规划中有重要的应用，为城市环境中重大事件的影响提供了及时的评估。
Analyzing the impact of disastrous events has been central to understanding and responding to crises. Traditionally, the assessment of disaster impact has primarily relied on the manual collection and analysis of surveys and questionnaires as well as the review of authority reports. This can be costly and time-consuming, whereas a timely assessment of an event’s impact is critical for crisis management and humanitarian operations. In this work, we formulate the impact discovery as the problem to identify the shared and discriminative subspace via tensor factorization due to the multi-dimensional nature of mobility data. Existing work in mining the shared and discriminative subspaces typically requires the predefined number of either type of them. In the context of event impact discovery, this could be impractical, especially for those unprecedented events. To overcome this, we propose a new framework, called “PairFac,” that jointly factorizes the multi-dimensional data to discover the latent mobility pattern along with its associated discriminative weight. This framework does not require splitting the shared and discriminative subspaces in advance and at the same time automatically captures the persistent and changing patterns from multi-dimensional behavioral data. Our work has important applications in crisis management and urban planning, which provides a timely assessment of impacts of major events in the urban environment.