Canonical correlation analysis; An overview with application to learning methods
Canonical correlation analysis; An overview with application to learning methods
We present a general method using kernel Canonical Correlation Analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation and enables a comparison between the text and images. In the experiments we look at two approaches of retrieving images based only on their content from a text query. We compare the approaches against a standard cross-representation retrieval technique known as the Generalised Vector Space Model.
Canonical correlation analysis, kernel canonical correlation analysis, partial Gram-Schmidt orthogonolisation, Cholesky decomposition, incomplete Cholesky decomposition, kernel methods.
Hardoon, David R.
05549e24-da95-4690-a3e2-3c672d2342b8
Szedmak, Sandor
c6a84aa3-2956-4acf-8293-a1b676f6d7d8
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
May 2003
Hardoon, David R.
05549e24-da95-4690-a3e2-3c672d2342b8
Szedmak, Sandor
c6a84aa3-2956-4acf-8293-a1b676f6d7d8
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Hardoon, David R., Szedmak, Sandor and Shawe-Taylor, John
(2003)
Canonical correlation analysis; An overview with application to learning methods
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Abstract
We present a general method using kernel Canonical Correlation Analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation and enables a comparison between the text and images. In the experiments we look at two approaches of retrieving images based only on their content from a text query. We compare the approaches against a standard cross-representation retrieval technique known as the Generalised Vector Space Model.
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tech_report03.pdf
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More information
Published date: May 2003
Keywords:
Canonical correlation analysis, kernel canonical correlation analysis, partial Gram-Schmidt orthogonolisation, Cholesky decomposition, incomplete Cholesky decomposition, kernel methods.
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 259225
URI: http://eprints.soton.ac.uk/id/eprint/259225
PURE UUID: bf4d17ba-8af1-44cb-a44e-58a00160b896
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Date deposited: 24 Mar 2004
Last modified: 14 Mar 2024 06:21
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Contributors
Author:
David R. Hardoon
Author:
Sandor Szedmak
Author:
John Shawe-Taylor
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