Learning a common substructure of multiple graphical Gaussian models
Learning a common substructure of multiple graphical Gaussian models
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DOI：
10.1016/j.neunet.2012.11.004
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发表时间：
2013-02-01
期刊：
影响因子：
7.8
通讯作者：
Washio, Takashi
中科院分区：
文献类型：
--
作者：
Hara, Satoshi;Washio, Takashi
数据的属性经常会因采样情况而异，这些情况通常会随着时间的推移或环境影响而沿着变化。分析这些数据的一种方法是找到不变性，或者在变化中保持不变的代表性特征。本文的目的是确定这样一个功能，即在不同条件下收集的多个数据集中常见的变量之间的相互作用或依赖关系。为此，我们提出了一个共同的子结构学习（CSSL）框架的基础上的图形高斯模型。我们进一步提出了一个简单的学习算法的基础上的双重增广拉格朗日和交替方向法的乘法器。我们确认CSSL的性能超过其他现有的技术，在多个数据集，通过数值模拟合成数据，并通过真实的世界的应用异常检测在汽车传感器中找到不变的依赖结构。(c)2012爱思唯尔有限公司保留所有权利。
Properties of data are frequently seen to vary depending on the sampled situations, which usually change along a time evolution or owing to environmental effects. One way to analyze such data is to find invariances, or representative features kept constant over changes. The aim of this paper is to identify one such feature, namely interactions or dependencies among variables that are common across multiple datasets collected under different conditions. To that end, we propose a common substructure learning (CSSL) framework based on a graphical Gaussian model. We further present a simple learning algorithm based on the Dual Augmented Lagrangian and the Alternating Direction Method of Multipliers. We confirm the performance of CSSL over other existing techniques in finding unchanging dependency structures in multiple datasets through numerical simulations on synthetic data and through a real world application to anomaly detection in automobile sensors. (c) 2012 Elsevier Ltd. All rights reserved.