Dimensionality Reduction by Weighted Connections between Neighborhoods
Joint Authors
Xie, Fuding
Fan, Yutao
Zhou, Ming
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-22
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Xie, Fuding& Fan, Yutao& Zhou, Ming. 2014. Dimensionality Reduction by Weighted Connections between Neighborhoods. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-5.
https://search.emarefa.net/detail/BIM-1034105
Modern Language Association (MLA)
Xie, Fuding…[et al.]. Dimensionality Reduction by Weighted Connections between Neighborhoods. Abstract and Applied Analysis No. 2014 (2014), pp.1-5.
https://search.emarefa.net/detail/BIM-1034105
American Medical Association (AMA)
Xie, Fuding& Fan, Yutao& Zhou, Ming. Dimensionality Reduction by Weighted Connections between Neighborhoods. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-5.
https://search.emarefa.net/detail/BIM-1034105
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1034105