Graph embedding plays an important role in dimensionality reduction for processing the high-dimensional data. In graph embedding, its keys are the different kinds of graph constructions that determine the performance of dimensionality reduction. Inspired by this fact, in this article we propose a novel graph embedding method named the double graphs-based discriminant projections (DGDP) by integrating two designed discriminative global graph constructions. The proposed DGDP can well discover the discriminant and geometrical structures of the high-dimensional data through the informative graph constructions. In two global graph constructions, we consider the geometrical distribution of each point on each edge of the graphs to define the adjacent weights with class information. Moreover, in the weight definition of one graph construction, we further strengthen pattern discrimination among all the classes to design the weights of the corresponding adjacent graph. To demonstrate the effectiveness of the proposed DGDP, we experimentally compare it with the state-of-the-art graph embedding methods on several data sets. The experimental results show that the proposed graph embedding method outperforms the competing methods with more power of data representation and pattern discrimination in the embedded subspace.

图形嵌入在处理高维数据的降维中起着重要作用。在图嵌入中，其关键是确定降维性能的不同类型的图构造。受这一事实的启发，在本文中，我们通过集成两个设计的判别全局图构造，提出了一种新颖的图嵌入方法，称为基于双重图的判别投影（DGDP）。拟议的DGDP可以通过信息图构造很好地发现高维数据的判别和几何结构。在两个全局图构造中，我们考虑了图的每个边缘上每个点的几何分布，以定义具有类信息的相邻权重。此外，在一个图结构的权重定义中，我们将进一步加强所有类别之间的模式判别，以设计相应相邻图的权重。为了证明所提出的DGDP的有效性，我们在几个数据集上通过实验将其与最新的图形嵌入方法进行了比较。实验结果表明，所提出的图形嵌入方法在嵌入子空间中具有优于数据竞争和模式识别能力的竞争方法。