Weighted Linear Discriminant Analysis based on Class Saliency Information
This is an incremental improvement for facial image classification tasks with imbalanced data.
The paper tackles the problem of imbalanced classes in Linear Discriminant Analysis by proposing a new variant that uses class saliency information to redefine scatter matrices, showing improvements in facial image classification on public datasets.
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.