Self-organizing Isometric Embedding Based on Statistical Criterions

  • Ruiguo Yu
  • Yuexian Hou
  • Pilian He
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


Popular nonlinear dimensionality reduction algorithms, e.g., LLE, Isomap and SIE suffer a difficulty in common: neighborhood parameter has to be configured in advance to gain meaningful embedding results. Simulation shows that embedding often loses relevance under improper parameters configures. But current embedding residual criterions of neighborhood parameters selection are not independent to neighborhood parameters. Therefore it cannot work universally. To improve the availability of nonlinear dimensionality reduction algorithms in the field of self-adaptive machine learning, it is necessary to find some transcendent criterions to achieve unsupervised parameters selection. This paper begins with a discussion of optimal embedding principles and proposes a statistics based on spatial mutual information and normalized dependency index spectrum to determine reasonable parameters configuration. The simulation supports our proposal effectively.


Mutual Information Statistical Criterion Geodesic Distance Kolmogorov Complexity Simple Principle 
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

  • Ruiguo Yu
    • 1
  • Yuexian Hou
    • 1
  • Pilian He
    • 1
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina

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