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Weighted Linear Discriminant Analysis Based on Class Saliency Information

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


Original languageEnglish
Title of host publication2018 25th IEEE International Conference on Image Processing (ICIP)
ISBN (Electronic)978-1-4799-7061-2
Publication statusPublished - 2018
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Image Processing -
Duration: 7 Oct 201810 Oct 2018

Publication series

ISSN (Electronic)2381-8549


ConferenceIEEE International Conference on Image Processing


In this paper, we propose a new variant of Linear Discriminant Analysis to overcome underlying drawbacks of traditional LDA and other LDA variants targeting problems involving imbalanced classes. Traditional LDA sets assumptions related to Gaussian class distribution and neglects influence of outlier classes, that might hurt in performance. We exploit intuitions coming from a probabilistic interpretation of visual saliency estimation in order to define saliency of a class in multi-class setting. Such information is then used to redefine the between-class and within-class scatters in a more robust manner. Compared to traditional LDA and other weight-based LDA variants, the proposed method has shown certain improvements on facial image classification problems in publicly available datasets.

Publication forum classification

Field of science, Statistics Finland