Learning a common substructure of multiple graphical Gaussian models

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• 作者：Satoshi Hara ; ^{hara@ar.sanken.osaka-u.ac.jp} ; Takashi Washio
• 关键词：Graphical Gaussian model ; Common substructure ; Dual Augmented Lagrangian ; Alternating Direction Method of Multipliers
• 刊名：Neural Networks
• 出版年：2013
• 出版时间：February, 2013
• 年：2013
• 卷：38
• 期：Complete
• 页码：23-38
• 全文大小：456 K

文摘

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.