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Two-dimensional discriminant analysis based on Schatten p-norm for image feature extraction

机译：基于Schatten p范数的二维判别分析用于图像特征提取

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A Schatten p-norm-based two-dimensional principal component analysis (2DPCA-SP) method was proposed for image feature extraction in our previous work. As an unsupervised method, 2DPCA-SP ignores the label information of training samples, which is essential to classification tasks. In this paper, we propose a novel Schatten p-norm-based two-dimensional discriminant analysis (2DDA-SP) method for image feature extraction, which learns an optimal projection matrix by maximizing the difference of Schatten p-norm-based between-class dispersion and Schatten p-norm-based within-class dispersion in low-dimensional feature space. By using both the Schatten p-norm metric and the label information of training samples, 2DDA-SP not only can efficiently extract discriminative features, but is also robust to outliers. We also propose an efficient iterative algorithm to solve the optimization problem of 2DDA-SP with 0 < p < 1. Experimental results on several image databases show that 2DDA-SP with 0 < p < 1 is effective and robust for image feature extraction. (C) 2017 Elsevier Inc. All rights reserved.
机译：在我们先前的工作中，提出了一种基于Schatten p范数的二维主成分分析（2DPCA-SP）方法用于图像特征提取。作为一种无监督的方法，2DPCA-SP忽略了训练样本的标签信息，这对于分类任务至关重要。在本文中，我们提出了一种新颖的基于Schatten p范数的二维判别分析（2DDA-SP）图像特征提取方法，该方法通过最大化基于Schatten p范数的类之间的差异来学习最佳投影矩阵低维特征空间中的色散和基于Schatten p范数的类内色散。通过同时使用Schatten p范数度量和训练样本的标签信息，2DDA-SP不仅可以有效地提取判别特征，而且对异常值具有鲁棒性。我们还提出了一种有效的迭代算法来解决0 <1的2DDA-SP的优化问题。在多个图像数据库上的实验结果表明，0 <1的2DDA-SP对于图像特征提取是有效且鲁棒的。 （C）2017 Elsevier Inc.保留所有权利。

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