Subspace Learning with Enriched Databases Using Symmetry

Konstantinos Papachristou, Anastasios Tefas, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)

Abstract

Principal Component Analysis and Linear Discriminant Analysis are of the most known subspace learning techniques. In this paper, a way for training set enrichment is proposed in order to improve the performance of the subspace learning techniques by exploiting the a-priori knowledge that many types of data are symmetric. Experiments on artificial, facial expression recognition, face recognition and object categorization databases denote the robustness of the proposed approach.
Original languageEnglish
Title of host publicationIntelligent Data analysis and its Applications, Volume I
Subtitle of host publicationProceeding of the First Euro-China Conference on Intelligent Data Analysis and Applications, June 13-15, 2014, Shenzhen, China
Pages113-122
Number of pages10
Volume297
ISBN (Electronic)978-3-319-07776-5
Publication statusPublished - 13 Jun 2014
EventFirst Euro-China Conference on Intelligent Data Analysis and Applications (ECC) - Shenzhen, China
Duration: 13 Jun 201415 Jun 2014

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer International Publishing
Volume297
ISSN (Print)2194-5357

Conference

ConferenceFirst Euro-China Conference on Intelligent Data Analysis and Applications (ECC)
CountryChina
CityShenzhen
Period13/06/1415/06/14

Keywords

  • Subspace Learning
  • Data Enrichment
  • Symmetry
  • Principal Component Analysis
  • Linear Discriminant Analysis

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