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Canonical Correlation Analysis: An Overview with Application to Learning Methods
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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Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
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Metadata
- Authors
- Hardoon, D. R., Szedmak, S., & Shawe-Taylor, J.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 2004-12-01
- Volume
- 16
- Issue
- 12
- Pages
- 2639-2664
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2100235303
- Cited by
- 3,202
- References
- 1
- FWCI
- 27.17
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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