In this paper, a novel least square regression method, named group low-rank representation-based discriminant linear regression (GLRRDLR), is proposed for multi-class classification. Unlike the conventional linear regression methods, the proposed method aims to learn a more discriminative projection. Specially, two main techniques are adopted to improve the discriminability of the projection. The first approach is to make the transformed samples locate in their own subspace by introducing a group low-rank constraint to the model, such that the distance between samples from the same class can be decreased greatly. The second approach is to simultaneously learn a discriminative target matrix for regression. The extensive experimental results show that the proposed method performs much better than the state-of-the-art methods, which proves the effectiveness of the above two approaches in improving the discriminability of the projection.

中文翻译：

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基于组低秩表示的判别线性回归

在本文中，提出了一种新的最小二乘回归方法，称为基于组低秩表示的判别线性回归（GLRRDLR），用于多类分类。与传统的线性回归方法不同，所提出的方法旨在学习更具辨别力的投影。特别地，采用了两种主要技术来提高投影的可辨别性。第一种方法是通过在模型中引入组低秩约束，使变换后的样本位于自己的子空间中，这样可以大大减少来自同一类的样本之间的距离。第二种方法是同时学习用于回归的判别目标矩阵。大量的实验结果表明，所提出的方法比最先进的方法性能要好得多，