Discriminant structure embedding for image recognition
Discriminant structure embedding for image recognition
复制标题
用于图像识别的判别结构嵌入
DOI：
10.1016/j.neucom.2015.09.071
复制
发表时间：
2016-01-22
期刊：
影响因子：
6
通讯作者：
Wang, Yong
中科院分区：
文献类型：
--
作者：
Miao, Shuo;Wang, Jing;Wang, Yong
邻域保持嵌入（NPE）已被广泛用于学习数据的内在结构。然而，它可能会损害局部拓扑并忽略数据的多样性。在本文中，我们提出了一种降维方法，即判别邻域结构嵌入（DNSE）。 DNSE构建邻接图来表征数据的多样性，并结合NPE来学习局部内在几何结构，很好地表征了相似性和多样性。最后，将LDA得到的全局结构与上述局部结构相结合，构建目标函数。对四个图像数据库的实验说明了该方法的有效性。 (C) 2015 Elsevier B.V. 保留所有权利。
Neighborhood preserving embedding (NPE) has been widely used to learn the intrinsic structure of data. However, it may impair the local topology and ignore the diversity of data. In this paper, we present a dimensionality reduction approach, namely discriminant neighborhood structure embedding (DNSE). DNSE constructs an adjacency graph to characterize the diversity of data and combines NPE to learn the local intrinsic geometric structure, which well characterizes both similarity and diversity. Finally, the global structure, which is obtained by LDA, is integrated with the aforementioned local structure to build the objective function. Experiments on the four image databases illustrate the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.