Sciweavers

PR
2007

Robust kernel Isomap

13 years 11 months ago
Robust kernel Isomap
Isomap is one of widely-used low-dimensional embedding methods, where geodesic distances on a weighted graph are incorporated with the classical scaling (metric multidimensional scaling). In this paper we pay our attention to two critical issues that were not considered in Isomap, such as: (1) generalization property (projection property); (2) topological stability. Then we present a robust kernel Isomap method, armed with such two properties. We present a method which relates the Isomap to Mercer kernel machines, so that the generalization property naturally emerges, through kernel principal component analysis. For topological stability, we investigate the network flow in a graph, providing a method for eliminating critical outliers. The useful behavior of the robust kernel Isomap is confirmed through numerical experiments with several data sets. Key words: Isomap, Kernel PCA, Manifold learning, Multidimensional scaling (MDS), Nonlinear dimensionality reduction.
Heeyoul Choi, Seungjin Choi
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where PR
Authors Heeyoul Choi, Seungjin Choi
Comments (0)