A hybrid semi-supervised topic model

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Latent topic models are used to analyze the low-dimensional semantic meaning of documents and images, which are widely applied to object categorization. However, object labeling is expensive and subjective in real applications. Thus, a hybrid semi-supervised topic model is proposed, which uses a small amount of labels to help the generative topic model find semantic topics and cluster the unlabeled data to the same class. We applied the model to obtain the semi-supervised LDA and pLSA methods. Experimental results on natural scene and head pose classification tasks show that the proposed method remains promising using only partial labels in the training process, which demonstrates the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationIntelligent Science and Intelligent Data Engineering - Second Sino-Foreign-Interchange Workshop, IScIDE 2011, Revised Selected Papers
Pages309-317
Number of pages9
DOIs
StatePublished - 2012
Event2nd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2011 - Xi'an, China
Duration: 23 Oct 201125 Oct 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7202 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2011
Country/TerritoryChina
CityXi'an
Period23/10/1125/10/11

Keywords

  • object categorization
  • Semi-supervised learning
  • topic model

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