Institute of Scientific and Industrial Research (ISIR), Osaka University, Osaka, 5670047, Japan.
Neural Netw. 2013 Feb;38:23-38. doi: 10.1016/j.neunet.2012.11.004. Epub 2012 Nov 17.
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.
数据的属性经常会随着采样情况的变化而变化，这种变化通常是沿着时间演化或者由于环境影响而产生的。分析这类数据的一种方法是寻找不变量，即保持不变的代表性特征，以应对变化。本文的目的是识别这样一个特征，即在不同条件下收集的多个数据集之间，变量之间的相互作用或依赖关系是共同存在的。为此，我们提出了一种基于图形高斯模型的共同子结构学习（CSSL）框架。我们进一步提出了一种简单的学习算法，该算法基于对偶增广拉格朗日法和交替方向乘子法。我们通过在合成数据上的数值模拟和在汽车传感器异常检测中的实际应用，确认了 CSSL 在多个数据集的不变依赖结构的发现上，相较于其他现有技术的性能。
