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Factorized Topic Models
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (CVAP)ORCID iD: 0000-0002-8640-9370
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (Computer Vision and Active Perception (CVAP) Lab)
The University of Sheffield.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-5750-9655
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present a modification to a latent topic model, which makes themodel exploit supervision to produce a factorized representation of the observeddata. The structured parameterization separately encodes variance that is sharedbetween classes from variance that is private to each class by the introduction of anew prior over the topic space. The approach allows for a more efficient inferenceand provides an intuitive interpretation of the data in terms of an informative signaltogether with structured noise. The factorized representation is shown to enhanceinference performance for image, text, and video classification.

Place, publisher, year, edition, pages
2013.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-134126OAI: oai:DiVA.org:kth-134126DiVA: diva2:664980
Conference
International Conference on Learning Representations
Note

QC 20131217

Available from: 2013-11-18 Created: 2013-11-18 Last updated: 2013-12-17Bibliographically approved

Open Access in DiVA

Factorized Topic Models(430 kB)97 downloads
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f21ad1cf343ed03a406622fb897b93b12858727bd866a23f2005fd141e2a4cbdc2dd8e4fcd4e9203643e1824ed021d7c8733d701a0739d87a7a3635089db830c
Type fulltextMimetype application/pdf

Authority records BETA

Zhang, ChengKjellström, Hedvig

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf