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Dimensionality Reduction by Weighted Connections between Neighborhoods

Author

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  • Fuding Xie
  • Yutao Fan
  • Ming Zhou

Abstract

Dimensionality reduction is the transformation of high‐dimensional data into a meaningful representation of reduced dimensionality. This paper introduces a dimensionality reduction technique by weighted connections between neighborhoods to improve K‐Isomap method, attempting to preserve perfectly the relationships between neighborhoods in the process of dimensionality reduction. The validity of the proposal is tested by three typical examples which are widely employed in the algorithms based on manifold. The experimental results show that the local topology nature of dataset is preserved well while transforming dataset in high‐dimensional space into a new dataset in low‐dimensionality by the proposed method.

Suggested Citation

  • Fuding Xie & Yutao Fan & Ming Zhou, 2014. "Dimensionality Reduction by Weighted Connections between Neighborhoods," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:928136
    DOI: 10.1155/2014/928136
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    References listed on IDEAS

    as
    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    2. Warren Torgerson, 1952. "Multidimensional scaling: I. Theory and method," Psychometrika, Springer;The Psychometric Society, vol. 17(4), pages 401-419, December.
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