Spectral kernels for classification

Li, Wenyuan, Ong, Kok-Leong, Ng, Wee-Keong and Sun, Aixim 2005, Spectral kernels for classification, Lecture notes in computer science, vol. 3589, pp. 520-529, doi: 10.1007/11546849_51.

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Title Spectral kernels for classification
Author(s) Li, Wenyuan
Ong, Kok-Leong
Ng, Wee-Keong
Sun, Aixim
Journal name Lecture notes in computer science
Volume number 3589
Start page 520
End page 529
Publisher Springer-Verlag
Place of publication Berlin, Germany
Publication date 2005
ISSN 0302-9743
Summary Spectral methods, as an unsupervised technique, have been used with success in data mining such as LSI in information retrieval, HITS and PageRank in Web search engines, and spectral clustering in machine learning. The essence of success in these applications is the spectral information that captures the semantics inherent in the large amount of data required during unsupervised learning. In this paper, we ask if spectral methods can also be used in supervised learning, e.g., classification. In an attempt to answer this question, our research reveals a novel kernel in which spectral clustering information can be easily exploited and extended to new incoming data during classification tasks. From our experimental results, the proposed Spectral Kernel has proved to speedup classification tasks without compromising accuracy.
Language eng
DOI 10.1007/11546849_51
Field of Research 080699 Information Systems not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2005, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30008929

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