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International Journal of Computer Vision

, Volume 91, Issue 2, pp 216–232 | Cite as

Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications

  • Tae-Kyun Kim
  • Björn Stenger
  • Josef Kittler
  • Roberto Cipolla
Article

Abstract

This paper presents an incremental learning solution for Linear Discriminant Analysis (LDA) and its applications to object recognition problems. We apply the sufficient spanning set approximation in three steps i.e. update for the total scatter matrix, between-class scatter matrix and the projected data matrix, which leads an online solution which closely agrees with the batch solution in accuracy while significantly reducing the computational complexity. The algorithm yields an efficient solution to incremental LDA even when the number of classes as well as the set size is large. The incremental LDA method has been also shown useful for semi-supervised online learning. Label propagation is done by integrating the incremental LDA into an EM framework. The method has been demonstrated in the task of merging large datasets which were collected during MPEG standardization for face image retrieval, face authentication using the BANCA dataset, and object categorisation using the Caltech101 dataset.

Keywords

Linear discriminant analysis LDA Incremental learning Online learning Label propagation Semi-supervised learning Face image retrieval Object recognition Object categorisation Face authentication 

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Tae-Kyun Kim
    • 1
  • Björn Stenger
    • 2
  • Josef Kittler
    • 3
  • Roberto Cipolla
    • 4
  1. 1.Sidney Sussex CollegeUniversity of CambridgeCambridgeUK
  2. 2.Toshiba Research Europe LtdCambridgeUK
  3. 3.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK
  4. 4.Department of EngineeringUniversity of CambridgeCambridgeUK

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