HomeRun: Scalable Sparse-Spectrum Reconstruction of Aggregated Historical Data
HomeRun: Scalable Sparse-Spectrum Reconstruction of Aggregated Historical Data
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DOI：
10.14778/3236187.3236201
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发表时间：
2018-07-01
影响因子：
2.5
通讯作者：
Zadorozhny, Vladimir
中科院分区：
文献类型：
--
作者：
Almutairi, Faisal M.;Yang, Fan;Zadorozhny, Vladimir
从多个聚合的和可能重叠的报告中恢复事件的时间序列是历史数据融合中的主要挑战。其目标是从低分辨率样本的混合物中尽可能准确地重建较高分辨率的事件序列。例如，我们的目标可能是将重叠的每月麻疹感染人数分解为每周人数。在本文中，我们提出了一种新的数据分解方法，称为Homerun，它利用序列的另一种表示法来寻找目标序列的谱。更具体地说，我们使用离散余弦变换(DCT)作为稀疏字典，将问题描述为所谓的基寻踪，并施加非负性和光滑性约束。Homerun通过找到包含最大(最重要)系数的目标序列的最稀疏频谱表示来利用DCT的能量压缩特性。我们利用乘法器的交替方向方法，以可伸缩和节省内存的步骤来解决所产生的优化问题。使用真实流行病学数据的实验表明，我们的方法远远优于最先进的技术，特别是在序列的DCT具有高度能量压缩的情况下。
Recovering a time sequence of events from multiple aggregated and possibly overlapping reports is a major challenge in historical data fusion. The goal is to reconstruct a higher resolution event sequence from a mixture of lower resolution samples as accurately as possible. For example, we may aim to disaggregate overlapping monthly counts of people infected with measles into weekly counts. In this paper, we propose a novel data disaggregation method, called HOMERUN, that exploits an alternative representation of the sequence and finds the spectrum of the target sequence. More specifically, we formulate the problem as so-called basis pursuit using the Discrete Cosine Transform (DCT) as a sparsifying dictionary and impose non-negativity and smoothness constraints. HOMERUN utilizes the energy compaction feature of the DCT by finding the sparsest spectral representation of the target sequence that contains the largest (most important) coefficients. We leverage the Alternating Direction Method of Multipliers to solve the resulting optimization problem with scalable and memory efficient steps. Experiments using real epidemiological data show that our method considerably outperforms the state-of-the-art techniques, especially when the DCT of the sequence has a high degree of energy compaction.