A Kernel Based Neighborhood Discriminant Submanifold Learning for Pattern Classification
Author
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-04
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
We propose a novel method, called Kernel Neighborhood Discriminant Analysis (KNDA), which can be regarded as a supervised kernel extension of Locality Preserving Projection (LPP).
KNDA nonlinearly maps the original data into a kernel space in which two graphs are constructed to depict the within-class submanifold and the between-class submanifold.
Then a criterion function which minimizes the quotient between the within-class representation and the between-class representation of the submanifolds is designed to separate each submanifold constructed by each class.
The real contribution of this paper is that we bring and extend the submanifold based algorithm to a general model and by some derivation a simple result is given by which we can classify a given object to a predefined class effectively.
Experiments on the MNIST Handwritten Digits database, the Binary Alphadigits database, the ORL face database, the Extended Yale Face Database B, and a downloaded documents dataset demonstrate the effectiveness and robustness of the proposed method.
American Psychological Association (APA)
Zhao, Xu. 2014. A Kernel Based Neighborhood Discriminant Submanifold Learning for Pattern Classification. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-510726
Modern Language Association (MLA)
Zhao, Xu. A Kernel Based Neighborhood Discriminant Submanifold Learning for Pattern Classification. Journal of Applied Mathematics No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-510726
American Medical Association (AMA)
Zhao, Xu. A Kernel Based Neighborhood Discriminant Submanifold Learning for Pattern Classification. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-510726
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-510726