An Incremental Nonlinear Dimensionality Reduction Algorithm Based on ISOMAP

Recently, there are several nonlinear dimensionality reduction algorithms that can discover the low-dimensional coordinates on a manifold based on training samples, such as ISOMAP, LLE, Laplacian eigenmaps. However, most of these algorithms work in batch mode. In this paper, we presented an incremental nonlinear dimensionality reduction algorithm to efficiently map new samples into the embedded space. The method permits one to select some landmark points and to only preserve geodesic distances between new data and landmark points. Self-organizing map algorithm is used to choose landmark points. Experiments demonstrate that the proposed algorithm is effective.

Authors and Affiliations

1. School of Computer Science and Engineering, Hebei University of Technology, Tianjin, 300130, China

   Lukui Shi & Enhai Liu

2. School of Electronic and Information Engineering, Tianjin University, Tianjin, 300072, China

   Lukui Shi & Pilian He

Editors and Affiliations

1. Guangxi Normal University, College of CS and IT, Guilin, China, and University of Technology, Faculty of Engineering and Information Technology, Sydney, Australia

   Shichao Zhang

2. Department of Electrical and Computer Systems Engineering, Monash University, 3800, Melbourne, Victoria, Australia

   Ray Jarvis

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© 2005 Springer-Verlag Berlin Heidelberg

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