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Spectral Nonlinearly Embedded Clustering Algorithm
Joint Authors
Zhang, Chen
Liu, Mingming
Sun, Wei
Liu, Bing
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-06-27
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
As is well known, traditional spectral clustering (SC) methods are developed based on the manifold assumption, namely, that two nearby data points in the high-density region of a low-dimensional data manifold have the same cluster label.
But, for some high-dimensional and sparse data, such an assumption might be invalid.
Consequently, the clustering performance of SC will be degraded sharply in this case.
To solve this problem, in this paper, we propose a general spectral embedded framework, which embeds the true cluster assignment matrix for high-dimensional data into a nonlinear space by a predefined embedding function.
Based on this framework, several algorithms are presented by using different embedding functions, which aim at learning the final cluster assignment matrix and a transformation into a low dimensionality space simultaneously.
More importantly, the proposed method can naturally handle the out-of-sample extension problem.
The experimental results on benchmark datasets demonstrate that the proposed method significantly outperforms existing clustering methods.
American Psychological Association (APA)
Liu, Mingming& Liu, Bing& Zhang, Chen& Sun, Wei. 2016. Spectral Nonlinearly Embedded Clustering Algorithm. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1112833
Modern Language Association (MLA)
Liu, Mingming…[et al.]. Spectral Nonlinearly Embedded Clustering Algorithm. Mathematical Problems in Engineering No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1112833
American Medical Association (AMA)
Liu, Mingming& Liu, Bing& Zhang, Chen& Sun, Wei. Spectral Nonlinearly Embedded Clustering Algorithm. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1112833
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112833