This paper addresses two fundamental problems: 1) learning discriminative model parameters and 2) avoiding over-fitting, which often occurs in regression-based classification tasks. We formulate these two problems in terms of relaxing both the strict binary label matrix and graph regularization term into more flexible forms so that the margins between different classes are enlarged as much as possible and the problem of over-fitting is avoided to some extent. This task is accomplished by the proposed double relaxed regression (DRR) method. The convex problem of DRR is solved efficiently with an iterative procedure. Extensive experiments on synthetic and real world image data sets demonstrate the effectiveness of the proposed method in terms of both classification accuracy and running time.

中文翻译：

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图像分类的双重松弛回归

本文解决了两个基本问题：1) 学习判别模型参数和 2) 避免过度拟合，这在基于回归的分类任务中经常发生。我们将这两个问题从将严格的二元标签矩阵和图正则化项放宽为更灵活的形式来表述，以便尽可能扩大不同类之间的边距，并在一定程度上避免过拟合的问题。这项任务是通过提出的双松弛回归 (DRR) 方法完成的。DRR 的凸问题可以通过迭代过程有效地解决。在合成和真实世界图像数据集上的大量实验证明了所提出方法在分类精度和运行时间方面的有效性。