Composite kernel machines on kernel local fisher discriminant space for financial data mining
Shian-Chang Huang, Tung‐Kuang Wu · 2013
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.