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Abstract and Applied Analysis - Volume 2014 2014, Article ID 928136, 5 pages -

Research Article

School of Urban and Environmental Science, Liaoning Normal University, Liaoning, Dalian 116029, China

Academy of Mathematics and System Sciences, Chinese Academy of Science, Beijing 100080, China

Received 6 January 2014; Accepted 24 February 2014; Published 22 April 2014

Academic Editor: Caihong Li

Copyright © 2014 Fuding Xie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 -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.





Author: Fuding Xie, Yutao Fan, and Ming Zhou

Source: https://www.hindawi.com/



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