Incremental Fast Subclass Discriminant Analysis

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

Abstract

This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA). We present an exact and an approximate linear solution, along with an approximate kernelized variant. Extensive experiments on eight image datasets with different incremental batch sizes show the superiority of the proposed approach in terms of training time and accuracy being equal or close to fastSDA solution and outperforming other methods.
Original languageEnglish
Title of host publication2020 IEEE International Conference on Image Processing (ICIP)
PublisherIEEE
Pages1771-1775
Number of pages5
ISBN (Electronic)978-1-7281-6395-6
DOIs
Publication statusPublished - 1 Oct 2020
Publication typeA4 Article in conference proceedings
EventIEEE International Conference on Image Processing - United Arab Emirates, Abu Dhabi, United Arab Emirates
Duration: 25 Oct 202028 Oct 2020
https://2020.ieeeicip.org

Publication series

NameProceedings : International Conference on Image Processing
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549

Conference

ConferenceIEEE International Conference on Image Processing
Abbreviated titleICIP 2020
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period25/10/2028/10/20
Internet address

Keywords

  • Kernel
  • Training
  • Feature extraction
  • Task analysis
  • Training data
  • Computational modeling
  • Data models

Publication forum classification

  • Publication forum level 1

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