Liu Xiao-Zhang, Yang Guan
School of Computer Science, Dongguan University of Technology, Dongguan 523808, China.
School of Computer Science, Zhongyuan University of Technology, Zhengzhou 450007, China.
ScientificWorldJournal. 2014 Jan 22;2014:875090. doi: 10.1155/2014/875090. eCollection 2014.
Maximum margin criterion (MMC) is a well-known method for feature extraction and dimensionality reduction. However, MMC is based on vector data and fails to exploit local characteristics of image data. In this paper, we propose a two-dimensional generalized framework based on a block-wise approach for MMC, to deal with matrix representation data, that is, images. The proposed method, namely, block-wise two-dimensional maximum margin criterion (B2D-MMC), aims to find local subspace projections using unilateral matrix multiplication in each block set, such that in the subspace a block is close to those belonging to the same class but far from those belonging to different classes. B2D-MMC avoids iterations and alternations as in current bilateral projection based two-dimensional feature extraction techniques by seeking a closed form solution of one-side projection matrix for each block set. Theoretical analysis and experiments on benchmark face databases illustrate that the proposed method is effective and efficient.
最大间隔准则（MMC）是一种广为人知的特征提取和降维方法。然而，MMC基于向量数据，无法利用图像数据的局部特征。在本文中，我们提出了一种基于块方法的二维广义框架用于MMC，以处理矩阵表示数据，即图像。所提出的方法，即逐块二维最大间隔准则（B2D-MMC），旨在通过在每个块集中使用单边矩阵乘法来找到局部子空间投影，使得在子空间中一个块与属于同一类的块接近，但与属于不同类的块远离。B2D-MMC通过为每个块集寻求单边投影矩阵的闭式解，避免了当前基于双边投影的二维特征提取技术中的迭代和交替。在基准人脸数据库上的理论分析和实验表明，所提出的方法是有效且高效的。
