CUED Publications database

Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications

Kim, TK and Stenger, B and Kittler, J and Cipolla, R (2010) Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications. International Journal of Computer Vision. pp. 1-17. ISSN 0920-5691

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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. © 2010 Springer Science+Business Media, LLC.

Item Type: Article
Uncontrolled Keywords: Face authentication Face image retrieval Incremental learning Label propagation LDA Linear discriminant analysis Object categorisation Object recognition Online learning Semi-supervised learning
Divisions: Div F > Machine Intelligence
Depositing User: Cron job
Date Deposited: 16 Jul 2015 13:36
Last Modified: 08 Oct 2015 11:44
DOI: 10.1007/s11263-010-0381-3