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  Correlational Spectral Clustering

Blaschko, M., & Lampert, C. (2008). Correlational Spectral Clustering. In 2008 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). Piscataway, NJ, USA: IEEE.

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 Creators:
Blaschko, MB1, 2, Author           
Lampert, CH1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are
common in real world data sources, such as images with
text captions. Traditional spectral clustering algorithms either
assume that data can be represented by a single similarity
measure, or by co-occurrence matrices that are then
used in biclustering. In contrast, the proposed method uses
separate similarity measures for each data representation,
and allows for projection of previously unseen data that are
only observed in one representation (e.g. images but not
text). We show that this algorithm generalizes traditional
spectral clustering algorithms and show consistent empirical
improvement over spectral clustering on a variety of
datasets of images with associated text.

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 Dates: 2008-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1109/CVPR.2008.4587353
BibTex Citekey: 5069
 Degree: -

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Title: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008)
Place of Event: Anchorage, AK, USA
Start-/End Date: 2008-06-23 - 2008-06-28

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Title: 2008 IEEE Conference on Computer Vision and Pattern Recognition
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 8 Identifier: ISBN: 978-1-4244-2243-2