Open Access
2014 Dimensionality Reduction by Weighted Connections between Neighborhoods
Fuding Xie, Yutao Fan, Ming Zhou
Abstr. Appl. Anal. 2014: 1-5 (2014). DOI: 10.1155/2014/928136


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.


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Fuding Xie. Yutao Fan. Ming Zhou. "Dimensionality Reduction by Weighted Connections between Neighborhoods." Abstr. Appl. Anal. 2014 1 - 5, 2014.


Published: 2014
First available in Project Euclid: 2 October 2014

zbMATH: 07023329
Digital Object Identifier: 10.1155/2014/928136

Rights: Copyright © 2014 Hindawi

Vol.2014 • 2014
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