DocumentCode
3347881
Title
Notice of Retraction
Locally regular embedding
Author
Lu Tan ; Yanrong Chi
Author_Institution
Inst. of Stat. & Math., Shandong Univ. of Finance, Jinan, China
Volume
4
fYear
2011
fDate
26-28 July 2011
Firstpage
2133
Lastpage
2136
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Introducing the topological structure and regular topology structure, the purpose is to seek with regular topological structure of low dimensional data set, the structural topological structure regularity, and puts forward the measure to keep data set topology structure of local rules embedding method. Compared to nuclear feature mapping methods, such as Locally Linear Embedding, Laplacian Eigenmap and so on, low dimensional embedded result is approximately regular, and data classification has more natural connection. The last results prove the theory results show that this technique can greatly discover the topological structure of data, compared to the LLE and Laplacian Eigenmap.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Introducing the topological structure and regular topology structure, the purpose is to seek with regular topological structure of low dimensional data set, the structural topological structure regularity, and puts forward the measure to keep data set topology structure of local rules embedding method. Compared to nuclear feature mapping methods, such as Locally Linear Embedding, Laplacian Eigenmap and so on, low dimensional embedded result is approximately regular, and data classification has more natural connection. The last results prove the theory results show that this technique can greatly discover the topological structure of data, compared to the LLE and Laplacian Eigenmap.
Keywords
data structures; embedded systems; pattern classification; topology; Laplacian eigenmap; data classification; data set topology structure; locally linear embedding; nuclear feature mapping method; regular topology structure; Biology; Biomedical imaging; Educational institutions; Finance; Information processing; Laplace equations; Laplacian Eigenmap; regular topological structure; topological structure;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6022390
Filename
6022390
Link To Document