DocumentCode
526407
Title
Notice of Retraction
Maximum Generialized Fisher Criterion
Author
Bo Li ; De-Shuang Huang
Author_Institution
Coll. of Comput. Sci. & Technol., Wuhan Univ. of Sci. & Technol., Wuhan, China
Volume
6
fYear
2010
fDate
9-11 July 2010
Firstpage
349
Lastpage
352
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.
In this brief, a Maximum Generalized Fisher Criterion (MGFM) based on manifold learning is presented. The proposed algorithm integrates both class information and the manifold information with the aim at finding an optimal subspace to maximize a Fisher form, which can characterize the intra-class compactness of the neighboring points with identical class and the inter-class separability of the other points. The proposed algorithm is verified by experimental results on some benchmark data and shows that the proposed algorithm is effective and feasible.
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.
In this brief, a Maximum Generalized Fisher Criterion (MGFM) based on manifold learning is presented. The proposed algorithm integrates both class information and the manifold information with the aim at finding an optimal subspace to maximize a Fisher form, which can characterize the intra-class compactness of the neighboring points with identical class and the inter-class separability of the other points. The proposed algorithm is verified by experimental results on some benchmark data and shows that the proposed algorithm is effective and feasible.
Keywords
learning (artificial intelligence); benchmark data; manifold learning; maximum generialized Fisher criterion; neighboring points; Artificial neural networks; Dimensionality reduction; manifold learning; maximum generalized fisher criterion;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5563955
Filename
5563955
Link To Document