Linear discriminant analysis (LDA) is a classical statistical machine-learning method, which aims to find a linear data transformation increasing class discrimination in an optimal discriminant subspace. Traditional LDA sets assumptions related to the Gaussian class distributions and single-label data annotations. In this article, we propose a new variant of LDA to be used in multilabel classification tasks for dimensionality reduction on original data to enhance the subsequent performance of any multilabel classifier. A probabilistic class saliency estimation approach is introduced for computing saliency-based weights for all instances. We use the weights to redefine the between-class and within-class scatter matrices needed for calculating the projection matrix. We formulate six different variants of the proposed saliency-based multilabel LDA (SMLDA) based on different prior information on the importance of each instance for their class(es) extracted from labels and features. Our experiments show that the proposed SMLDA leads to performance improvements in various multilabel classification problems compared to several competing dimensionality reduction methods.

中文翻译：

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基于显着性的多标签线性判别分析


线性判别分析（LDA）是一种经典的统计机器学习方法，其目的是在最佳判别子空间中找到增加类别判别力的线性数据变换。传统的 LDA 设置与高斯类分布和单标签数据注释相关的假设。在本文中，我们提出了一种新的 LDA 变体，用于多标签分类任务，对原始数据进行降维，以增强任何多标签分类器的后续性能。引入概率类显着性估计方法来计算所有实例的基于显着性的权重。我们使用权重来重新定义计算投影矩阵所需的类间和类内散布矩阵。我们根据从标签和特征中提取的每个实例对于其类别的重要性的不同先验信息，制定了所提出的基于显着性的多标签 LDA (SMLDA) 的六种不同变体。我们的实验表明，与几种竞争的降维方法相比，所提出的 SMLDA 可以提高各种多标签分类问题的性能。