Canonical correlation analysis; An overview with application to learning methods
Hardoon, David R., Szedmak, Sandor and Shawe-Taylor, John (2003) 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.
|Item Type:||Monograph (Technical Report)|
|Keywords:||Canonical correlation analysis, kernel canonical correlation analysis, partial Gram-Schmidt orthogonolisation, Cholesky decomposition, incomplete Cholesky decomposition, kernel methods.|
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science
|Date Deposited:||24 Mar 2004|
|Last Modified:||02 Mar 2012 02:33|
|Contributors:||Hardoon, David R. (Author)
Szedmak, Sandor (Author)
Shawe-Taylor, John (Author)
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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