An application of the nearest correlation matrix to Web document classification
An application of the nearest correlation matrix to Web document classification
The Web document is organized by a set of textual data according to a predefined
logical structure. It has been shown that collecting Web documents with similar
structures can improve query efficiency. The XML document has no vectorial representation,
which is required in most existing classification algorithms. The kernel method has
been applied to represent structural data with pairwise similarity. In this case, a set of Web
data can be fed into classification algorithms in the format of a kernel matrix. However,
since the distance between a pair of Web documents is usually obtained approximately, the
derived distance matrix is not a kernel matrix. In this paper, we propose to use the nearest
correlation matrix (of the estimated distance matrix) as the kernel matrix, which can be
fast computed by a Newton-type method. Experimental studies show that the classification
accuracy can be significantly improved.
support vector machines, classification, kernel matrix, semidefinite programming.
701-713
Qi, Hou-Duo
e9789eb9-c2bc-4b63-9acb-c7e753cc9a85
Xia, Zhonghang
f59c1e13-40fb-44d9-bcaf-31643ccc6637
Xing, Guangming
3fb034a9-d19c-4531-9735-33967178e72c
November 2007
Qi, Hou-Duo
e9789eb9-c2bc-4b63-9acb-c7e753cc9a85
Xia, Zhonghang
f59c1e13-40fb-44d9-bcaf-31643ccc6637
Xing, Guangming
3fb034a9-d19c-4531-9735-33967178e72c
Qi, Hou-Duo, Xia, Zhonghang and Xing, Guangming
(2007)
An application of the nearest correlation matrix to Web document classification.
Journal of Industrial Management and Optimization, 3 (4), .
Abstract
The Web document is organized by a set of textual data according to a predefined
logical structure. It has been shown that collecting Web documents with similar
structures can improve query efficiency. The XML document has no vectorial representation,
which is required in most existing classification algorithms. The kernel method has
been applied to represent structural data with pairwise similarity. In this case, a set of Web
data can be fed into classification algorithms in the format of a kernel matrix. However,
since the distance between a pair of Web documents is usually obtained approximately, the
derived distance matrix is not a kernel matrix. In this paper, we propose to use the nearest
correlation matrix (of the estimated distance matrix) as the kernel matrix, which can be
fast computed by a Newton-type method. Experimental studies show that the classification
accuracy can be significantly improved.
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Published date: November 2007
Keywords:
support vector machines, classification, kernel matrix, semidefinite programming.
Organisations:
Operational Research
Identifiers
Local EPrints ID: 54536
URI: http://eprints.soton.ac.uk/id/eprint/54536
PURE UUID: 4b975279-ab8d-47b6-974b-ed2fee300bc7
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Date deposited: 28 Jul 2008
Last modified: 09 Jan 2022 03:17
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Contributors
Author:
Zhonghang Xia
Author:
Guangming Xing
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