English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
 Local TagsRelease HistoryDetailsSummary
  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.

Item is

 

show all hide all

Basic

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

Files

show Files

Locators

show
hide
Locator:
https://link.springer.com/content/pdf/10.1007%2F978-3-642-15552-9_41.pdf (Publisher version)
Description:
-
OA-Status:

Creators

show
hide
 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              

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s):
 Dates: 2010-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-642-15552-9_41
BibTex Citekey: 6639
 Degree: -

Event

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

Legal Case

show

Project information

show

Source 1

show
hide
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

Source 2

show
hide
Title: Lecture Notes in Computer Science
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 6312 Sequence Number: - Start / End Page: - Identifier: -