IEEE Trans Image Process. 2020;29:186-198. doi: 10.1109/TIP.2019.2926774. Epub 2019 Jul 22.
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.
投影学习被广泛应用于提取分类的判别特征。尽管已经提出了许多用于此目的的方法，但它们几乎没有在投影学习过程中探索标签信息，并且无法获得令人满意的性能。此外，许多现有的方法只能学习有限数量的投影来进行特征提取，这可能会降低识别性能。为了解决这些问题，我们提出了一种新的约束判别投影学习 (CDPL) 方法用于图像分类。具体来说，CDPL 可以被表述为子空间学习和分类的联合优化问题。所提出的方法结合了低秩约束来学习一个稳健的子空间，该子空间可用作连接原始视觉特征和目标输出的桥梁。采用回归函数来明确利用类标签信息，从而增强子空间的判别能力。与现有的方法不同，我们分别使用两个矩阵来执行特征学习和回归，从而使所提出的方法能够获得更多的投影，并在分类任务中实现更好的性能。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先进的方法的优势。
