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，旨在找到在最佳判别子空间中增加类判别的线性数据变换。传统的LDA设置与高斯类分布和单标签数据注释有关的假设。在本文中，我们提出了一种LDA的新变体，该变体将用于多标签分类任务中，以减少原始数据的维数，从而增强任何多标签分类器的后续性能。为了计算所有实例的基于显着性的权重，引入了概率类显着性估计方法。我们使用权重重新定义计算投影矩阵所需的类间和类内散布矩阵。我们基于关于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，制定了建议的基于显着性的多标签LDA（SMLDA）的六个不同变体。我们的实验表明，与几种竞争性降维方法相比，提出的SMLDA可以改善各种多标签分类问题的性能。