2-2 超图学习及其应用.pdf

1、代价敏感超图学习及其应用王楠 讲师|01Hypergraph Learning02Cost Sensitive Hypergraph Learning03Applications目录目录 CONTENT|Hypergraph Learning01|Hypergraph Learning|Graphv1v2v3v4v5v6v7v1v2v3v4v5v6v7e1e2e3HypergraphHypergraph Learning|目标公式目标公式最终超图学习的目标公式平滑数据之间的关系经验损失1 Zhou D,Huang J,Schlkopf B.Learning with hypergraphs:C

2、lustering,classification,and embedding,Advances in neural information processing systems.2006:1601-1608.Hypergraph Learning|目标公式目标公式在分类工作中需要被打标的测试数据已打标训练数据的标签信息经验损失Hypergraph Learning|最终超图学习的目标公式平滑数据之间的关系原则：超图上的超点之间连接超边越多，标签越相近。目标公式目标公式经验损失Hypergraph Learning|目标公式目标公式最终超图学习的目标公式有标签数据的经验性损失平滑数据之间的关系V

3、ertex Re-Weighting|异常攻击检测正常行为异常行为Vertex-weighted Hypergraph Learning|Nan Wang,Zizhao Zhang,Xibin Zhao,Quan Miao,Rongrong Ji,Yue Gao:Exploring High-Order Correlations for Industry Anomaly Detection.IEEE Trans.Ind.Electron.66(12):9682-9691(2019)Vertex-weighted Hypergraph Learning|数据权重初始化结合孤立值和相似值计算样例权

4、重1 Y.Zhang,L.Li,J.Zhou,X.Li,and Z.Zhou,“Anomaly detection with partially observed anomalies,”in Proc.Web Conf.,2018,pp.639646.Vertex-weighted Hypergraph Learning|计算未打标计算未打标数据与异常数据与异常簇的相似性簇的相似性孤立森林孤立森林分析分析已标记异常数据已标记异常数据未标记数据未标记数据聚类分析聚类分析计算相似性得分计算相似性得分异常数据更异常数据更容易被最早容易被最早孤立孤立计算孤立性得分计算孤立性得分根据数据质量根据数据质量

5、(相似性和孤立性相似性和孤立性)计算样例权重计算样例权重Vertex-weighted Hypergraph Learning|构建数据权重优化超图模型Vertex-weighted Hypergraph Learning|Compared Methods(1)Anomaly detection with partially observed anomalies(ADOA)1(2)Partial differential equation continuum limits(PDEs)2(3)Isolation forest(iForest)3(4)Oversampling principal

6、component analysis(osPCA)4(5)Nonnegative sparse graph based label propagation(NSGLP)5(6)Vertex-weighted hypergraph learning(V-HL)6Evaluation(1)Accuracy(2)AUC(3)Precision(4)PD(5)PF(6)1-measureOn industry anomaly detection dataset.On ODDS dataset1 Y.Zhang,L.Li,J.Zhou,X.Li,and Z.Zhou,“Anomaly detection