ML Anthology

Canonical Correlation Analysis: An Overview with Application to Learning Methods

David R. Hardoon, Sándor Szedmák, John Shawe-Taylor
NeCo 2004 pp. 2639-2664
doi:10.1162/0899766042321814 /neco/2004/hardoon2004neco-canonical/

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 on only their content from a text query. We compare orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model.

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Text

Hardoon et al. "Canonical Correlation Analysis: An Overview with Application to Learning Methods." Neural Computation, 2004. doi:10.1162/0899766042321814

Markdown

[Hardoon et al. "Canonical Correlation Analysis: An Overview with Application to Learning Methods." Neural Computation, 2004.](https://mlanthology.org/neco/2004/hardoon2004neco-canonical/) doi:10.1162/0899766042321814

BibTeX

@article{hardoon2004neco-canonical,
  title     = {{Canonical Correlation Analysis: An Overview with Application to Learning Methods}},
  author    = {Hardoon, David R. and Szedmák, Sándor and Shawe-Taylor, John},
  journal   = {Neural Computation},
  year      = {2004},
  pages     = {2639-2664},
  doi       = {10.1162/0899766042321814},
  volume    = {16},
  url       = {https://mlanthology.org/neco/2004/hardoon2004neco-canonical/}
}