Proceedings of the Korean Information Science Society Conference (한국정보과학회:학술대회논문집)
- 2005.07b
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- Pages.748-750
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- 2005
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- 1598-5164(pISSN)
Mercer Kernel Isomap
- Choi, Hee-Youl (Department of Computer Science, POSTECH) ;
- Choi, Seung-Jin (Department of Computer Science, POSTECH)
- Published : 2005.07.01
Abstract
Isomap [1] is a manifold learning algorithm, which extends classical multidimensional scaling (MDS) by considering approximate geodesic distance instead of Euclidean distance. The approximate geodesic distance matrix can be interpreted as a kernel matrix, which implies that Isomap can be solved by a kernel eigenvalue problem. However, the geodesic distance kernel matrix is not guaranteed to be positive semidefinite. In this paper we employ a constant-adding method, which leads to the Mercer kernel-based Isomap algorithm. Numerical experimental results with noisy 'Swiss roll' data, confirm the validity and high performance of our kernel Isomap algorithm.
Keywords