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Adaptively Incremental Self-organizing Isometric Embedding

  • Hou Yuexian
  • Gong Kefei
  • He Pilian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)

Abstract

In this paper, we propose an adaptive incremental nonlinear dimensionality reduction algorithm for data stream in adaptive Self-organizing Isometric Embedding [1][3] framework. Assuming that each sampling point of underlying manifold and its adaptive neighbors [3] can preserve the principal directions of the regions that they reside on, our algorithm need only update the geodesic distances between anchors and all the other points, as well as distances between neighbors of incremental points and all the other points when a new point arrives. Under the above assumption, our algorithms can realize an approximate linear time complexity embedding of incremental points and effectively tradeoff embedding precision and time cost.

Keywords

Short Path Principal Direction Geodesic Distance Kernel Principal Component Analysis Nonlinear Dimensionality Reduction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hou Yuexian
    • 1
  • Gong Kefei
    • 1
  • He Pilian
    • 1
  1. 1.School of Computer Science & TechnologyTianjin UniversityChina

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