Projection learning is widely used in extracting discriminative features for classification. Although numerous methods have already been proposed for this goal, they barely explore the label information during projection learning and fail to obtain satisfactory performance. Besides, many existing methods can learn only a limited number of projections for feature extraction which may degrade the performance in recognition. To address these problems, we propose a novel constrained discriminative projection learning (CDPL) method for image classification. Specifically, CDPL can be formulated as a joint optimization problem over subspace learning and classification. The proposed method incorporates the low-rank constraint to learn a robust subspace which can be used as a bridge to seamlessly connect the original visual features and objective outputs. A regression function is adopted to explicitly exploit the class label information so as to enhance the discriminability of subspace. Unlike existing methods, we use two matrices to perform feature learning and regression, respectively, such that the proposed approach can obtain more projections and achieve superior performance in classification tasks. The experiments on several datasets show clearly the advantages of our method against other state-of-the-art methods.

中文翻译：

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约束判别投影学习的图像分类。

投影学习被广泛用于提取区分特征以进行分类。尽管已经针对此目标提出了许多方法，但是它们在投影学习过程中几乎没有探索标签信息，并且无法获得令人满意的性能。此外，许多现有方法只能学习有限数量的用于特征提取的投影，这可能会降低识别性能。为了解决这些问题，我们提出了一种新颖的约束判别投影学习（CDPL）方法进行图像分类。具体而言，CDPL可以表示为子空间学习和分类上的联合优化问题。所提出的方法结合了低秩约束来学习鲁棒的子空间，该子空间可以用作无缝连接原始视觉特征和目标输出的桥梁。采用回归函数来显式利用类标签信息，以增强子空间的可分辨性。与现有方法不同，我们分别使用两个矩阵来进行特征学习和回归，从而使所提出的方法可以获取更多的投影并在分类任务中实现出色的性能。在几个数据集上的实验清楚地表明了我们的方法相对于其他最新方法的优势。