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  Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning

Lampert, C., & Kroemer, O. (2010). Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning. In K. Daniilidis, P. Maragos, & N. Paragios (Eds.), Computer Vision - ECCV 2010 (pp. 566-579). Berlin, Germany: Springer.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BE76-A Version Permalink: http://hdl.handle.net/21.11116/0000-0002-80DF-2
Genre: Conference Paper

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 Creators:
Lampert, CH, Author              
Kroemer, O1, 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 study the problem of multimodal dimensionality reduction assuming that data samples can be missing at training time, and not all data modalities may be present at application time. Maximum covariance analysis, as a generalization of PCA, has many desirable properties, but its application to practical problems is limited by its need for perfectly paired data. We overcome this limitation by a latent variable approach that allows working with weakly paired data and is still able to efficiently process large datasets using standard numerical routines. The resulting weakly paired maximum covariance analysis often finds better representations than alternative methods, as we show in two exemplary tasks: texture discrimination and transfer learning.

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 Dates: 2010-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-642-15552-9_41
BibTex Citekey: 6639
 Degree: -

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Title: 11th European Conference on Computer Vision
Place of Event: Heraklion, Greece
Start-/End Date: 2010-09-05 - 2010-09-11

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Title: Computer Vision - ECCV 2010
Source Genre: Proceedings
 Creator(s):
Daniilidis, K, Editor
Maragos, P, Editor
Paragios, N, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 566 - 579 Identifier: ISBN: 978-3-642-15552-9

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 6312 Sequence Number: - Start / End Page: - Identifier: -