大连理工大学数学科学学院
通知与公告

Anomaly Detection in Dynamic Networks using Multi-view Time-​Series Hypersphere Learning

2017年06月06日 15:36  点击:[]

报告题目:Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning

报告人: Xian Teng    University of Pittsburgh

报告时间:20176 16日(星期五)下午 15:00-16:00

报告地点:创新园大厦 A1101

校内联系人:张仁权      联系电话:84708351-8123

 

Abstract: Detecting anomalous patterns from dynamic and multi-attributed network systems has been a challenging problem due to the complication of temporal dynamics and the variations reflected in multiple data sources. We propose a Multi-view Time-Series Hypersphere Learning (MTHL) approach that leverages multi-view learning and support vector description to tackle this problem. Given a dynamic network with time-varying edge and node properties, MTHL projects multi-view time-series data into a shared latent subspace, and then learns a compact hypersphere surrounding normal samples with soft constraints. The learned hypersphere allows for effectively distinguishing normal and abnormal cases. We further propose an efficient, two-stage alternating optimization algorithm as a solution to the MTHL. Extensive experiments are conducted on both synthetic and real datasets. Results demonstrate that our method outperforms the state-of-the-art baseline methods in detecting three types of events that involve (i) time-varying features alone, (ii) time-aggregated features alone, as well as (iii) both features. Moreover, our approach exhibits consistent and good performance in face of issues including noises, anomaly pollution in training phase and data imbalance.

 

报告人介绍:滕贤,美国匹兹堡大学信息科学学院博士20157月毕业于北京航空航天大学数学与系统科学学院,获硕士学位。主要从事动态网络模型下的结构与动力学行为研究,以及基于网络特征的启发式优化算法研究。

 

上一条:Edge-Preserving Regularizations for tomography reconstruction in Mesh Domain 下一条:Efficient collective influence maximization in cascading processes with first-order transitions

关闭