Composite kernel machines on kernel local fisher discriminant space for financial data mining

研究成果: Conference contribution

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.

原文English
主出版物標題IET International Conference on Information and Communications Technologies, IETICT 2013
頁面58-63
頁數6
2013
版本618 CP
出版狀態Published - 2013 十二月 1
事件IET International Conference on Information and Communications Technologies, IETICT 2013 - Beijing, China
持續時間: 2013 四月 272013 四月 29

Other

OtherIET International Conference on Information and Communications Technologies, IETICT 2013
國家China
城市Beijing
期間13-04-2713-04-29

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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