An Incremental Nonlinear Dimensionality Reduction Algorithm Based on ISOMAP

Shi, Lukui; He, Pilian; Liu, Enhai

doi:10.1007/11589990_104

Lukui Shi^20,21,
Pilian He²¹ &
Enhai Liu²⁰

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3809))

Included in the following conference series:

Australasian Joint Conference on Artificial Intelligence

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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.

This work was supported by Science-Technology Development Project of Tianjin (No. 04310941R) and by Applied Basic Research Project of Tianjin (No. 05YFJMJC11700).

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Authors and Affiliations

School of Computer Science and Engineering, Hebei University of Technology, Tianjin, 300130, China
Lukui Shi & Enhai Liu
School of Electronic and Information Engineering, Tianjin University, Tianjin, 300072, China
Lukui Shi & Pilian He

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Lukui Shi
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Pilian He
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Enhai Liu
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Editors and Affiliations

Guangxi Normal University, College of CS and IT, Guilin, China, and University of Technology, Faculty of Engineering and Information Technology, Sydney, Australia
Shichao Zhang
Department of Electrical and Computer Systems Engineering, Monash University, 3800, Melbourne, Victoria, Australia
Ray Jarvis

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Shi, L., He, P., Liu, E. (2005). An Incremental Nonlinear Dimensionality Reduction Algorithm Based on ISOMAP. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_104

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DOI: https://doi.org/10.1007/11589990_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
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