GRATE: Granular Recovery of Aggregated Tensor Data by Example
GRATE: Granular Recovery of Aggregated Tensor Data by Example
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GRATE：通过示例对聚合张量数据进行粒度恢复
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
N. Sidiropoulos
中科院分区：
文献类型：
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作者：
Ahmed S. Zamzam;Bo Yang;N. Sidiropoulos
在本文中，我们解决了使用分解示例恢复汇总张量数据准确分解的挑战。这个问题是由多个应用程序激发的。例如，鉴于某些房屋的能源消耗细分，我们如何在其他房屋中分解同一时期消耗的总能量？为了应对这一挑战，我们提出了Grate，这是一种原则上的方法，将手头的任务变成了受限的张量分解问题。然后，使用交替的最小二乘算法解决此优化问题。 GRATE具有处理精确的聚合数据以及不精确聚集的能力，其中一些未观察到的数量有助于聚合数据。特殊重点是能源分类问题，目的是从消费者每月的总消费中为消费者提供能量破裂。两个实际数据集上的实验表明，与最先进的能量分解方法相比，炉排在恢复更准确的分解方面的功效。
In this paper, we address the challenge of recovering an accurate breakdown of aggregated tensor data using disaggregation examples. This problem is motivated by several applications. For example, given the breakdown of energy consumption at some homes, how can we disaggregate the total energy consumed during the same period at other homes? In order to address this challenge, we propose GRATE, a principled method that turns the ill-posed task at hand into a constrained tensor factorization problem. Then, this optimization problem is tackled using an alternating least-squares algorithm. GRATE has the ability to handle exact aggregated data as well as inexact aggregation where some unobserved quantities contribute to the aggregated data. Special emphasis is given to the energy disaggregation problem where the goal is to provide energy breakdown for consumers from their monthly aggregated consumption. Experiments on two real datasets show the efficacy of GRATE in recovering more accurate disaggregation than state-of-the-art energy disaggregation methods.
DOI：
10.1609/aaai.v31i1.11179
发表时间：
2017-02
期刊：
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影响因子：
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作者：
Nipun Batra;Hongning Wang;Amarjeet Singh;K. Whitehouse
通讯作者：
Nipun Batra;Hongning Wang;Amarjeet Singh;K. Whitehouse
Prema：从多个聚合视图恢复有原则的张量数据
影响因子：
7.5
作者：
Almutairi, Faisal M.;Kanatsoulis, Charilaos I.;Sidiropoulos, Nicholas D.
通讯作者：
Sidiropoulos, Nicholas D.