COMPOSITE KERNEL MACHINES ON KERNEL LOCAL FISHER DISCRIMINANT SPACE FOR FINANCIAL DATA MINING

摘要：

　　This paper proposes a novel approach to overcome the bottleneck in financial data mining.We construct a composite kernel machine (CKM) on the kernel local fisher discriminant space (KLFDS) to solve three problems in high-dimensional data mining: the curse of dimensionality,data complexity and nonlinearity.CKM exploits multiple data sources with strong capability to identify the relevant ones and their apposite kernel representation.KLFDS is an optimal projection of original data to a low dimensional space which maximizes the margin between data points from different classes at each local area of data manifold.Our new system robustly overcomes the weakness of CKM,it outperforms many traditional classification systems.

关键词： Financial Data Mining Multiple Kernel Learning Subspace Learning Kernel Local Fisher Discriminant Analysis Support Vector Machine

作者: Shian-Chang Huang Tung-Kuang Wu

作者单位: Department of Business Administration National Changhua University of Education,Changhua,Taiwan Department of Information Management National Changhua University of Education,Changhua,Taiwan

会议类型: 国际会议

会议名称: 2013IET International Conference on Information and Communication Technologies(IETICT2013)2013IET信息与通信技术国际会议

会议地点: 北京

会议语种:英文

页码: 45-50

在线出版日期: 2013-04-27（万方平台首次上网日期，不代表论文的发表时间）