# Weighted Linear Discriminant Analysis based on Class Saliency Information

arXiv: https://arxiv.org/abs/1802.06547 · HTML: https://mlsanity.com/paper/1802.06547/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Effects of Parametric and Non-Parametric Methods on High Dimensional Sparse Matrix Representations](https://mlsanity.com/paper/2202.02894/) (2022)

## Abstract (arXiv)

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.