An incremental nonlinear dimensionality reduction algorithm based on ISOMAP

Authors:
Lukui Shi;Pilian He;Enhai Liu
Affiliations:
School of Computer Science and Engineering, Hebei University of Technology, Tianjin, China;School of Electronic and Information Engineering, Tianjin University, Tianjin, China;School of Computer Science and Engineering, Hebei University of Technology, Tianjin, China
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

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.