Composite Kernel Machines on Kernel Locally Consistent Concept Factorization Space for Data Mining
Shian-Chang Huang 1,
Lung-Fu Chang 2, and
Tung-Kuang Wu 3
1. National Changhua University of Education, Department of Business Administration
2. National Taipei College of Business, Department of Finance
3. National Changhua University of Education, Department of Information Management
2. National Taipei College of Business, Department of Finance
3. National Changhua University of Education, Department of Information Management
Abstract—This paper proposes a novel approach to overcome the main problems in high-dimensional data mining. We construct a composite kernel machine (CKM) on a special space (the kernel locally consistent concept factorization (KLCCF) space) 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. KLCCF finds a compact representation of data, which uncovers the hidden information and simultaneously respects the intrinsic geometric structure of data manifold. Our new system robustly overcomes the weakness of CKM, it outperforms many traditional classification systems.
Index Terms—data mining, multiple kernel learning, kernel locally consistent concept factorization, manifold learning, support vector machine
Cite: Shian-Chang Huang, Lung-Fu Chang, and Tung-Kuang Wu, "Composite Kernel Machines on Kernel Locally Consistent Concept Factorization Space for Data Mining," International Journal of Signal Processing Systems, Vol. 2, No. 1, pp. 64-69, June 2014. doi: 10.12720/ijsps.2.1.64-69