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Canonical Correlation Analysis: An Overview with Application to Learning Methods

Authors:

David R. Hardoon,

Sandor R. Szedmak,

John R. Shawe-taylorAuthors Info & Claims

Neural Computation, Volume 16, Issue 12

Pages 2639 - 2664

https://doi.org/10.1162/0899766042321814

Published: 01 December 2004 Publication History

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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Cited By

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Jin RHou TJin TYuan JDu C(2024)A method for image–text matching based on semantic filtering and adaptive adjustmentJournal on Image and Video Processing10.1186/s13640-024-00639-y2024:1Online publication date: 29-Aug-2024
https://dl.acm.org/doi/10.1186/s13640-024-00639-y
Rohani Sarvestani RGholami ABoostani R(2024)Kernel Probabilistic Dependent-Independent Canonical Correlation AnalysisInternational Journal of Intelligent Systems10.1155/2024/73934312024Online publication date: 1-Jan-2024
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Canonical Correlation Analysis: An Overview with Application to Learning Methods
1. Computing methodologies
  1. Machine learning

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Published In

Neural Computation Volume 16, Issue 12

December 2004

226 pages

ISSN:0899-7667

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MIT Press

Cambridge, MA, United States

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Published: 01 December 2004

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https://dl.acm.org/doi/10.1186/s13640-024-00639-y
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Canonical random correlation analysis

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Deep canonical correlation analysis

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