Label propagation (LP) is one of the state-of-the-art graph based semi-supervised learning (GSSL) algorithm. Probability transition matrix (PTM) is the key for LP to propagate label information among samples. Conventionally, PTM is calculated based on the graph constructed in advance, and graph construction independent of PTM calculation. It leads to complex steps for acquiring PTM, and more importantly, brings about the lack of correlation between graph construction and inference. Based on adaptive neighbors-based method, probabilistic nearest neighbors (PNN) based graph construction algorithm is proposed for effective ℓ2 norm optimization, and the solving process of the objective function is optimized by incorporating min-max normalization. The derived PNN matrix is more discriminative and directly serve as PTM for LP. It makes PTM computation more conveniently and more applicable for classification task. In addition, number of neighbors is adaptively determined on the premise of its preset value. Experimental results show that the proposed PNN algorithm specializes in reflecting probability differences of neighboring nodes in a graph, and positive results are achieved in semi-supervised classification. The average classification accuracy on synthetic data sets is 84.24%, and that on image data sets achieves 89.08%.

标签传播（LP）是基于最新图形的半监督学习（GSSL）算法之一。概率转换矩阵（PTM）是LP在样本之间传播标签信息的关键。通常，PTM是基于预先构造的图来计算的，并且图的构造独立于PTM的计算。它导致获取PTM的步骤复杂，更重要的是，导致图构造与推理之间缺乏相关性。基于自适应近邻算法，提出了基于概率最近邻（PNN）的图构造算法进行有效的ℓ2范数优化，并结合最小-最大归一化对目标函数的求解过程进行了优化。派生的PNN矩阵更具判别力，可以直接用作LP的PTM。它使PTM计算更方便，更适用于分类任务。另外，在邻居的预设值的前提下自适应地确定邻居的数量。实验结果表明，所提出的PNN算法专门用于反映图中相邻节点的概率差异，在半监督分类中取得了积极的成果。合成数据集的平均分类精度为84.24％，图像数据集的平均分类精度达到89.08％。在半监督分类中取得了积极的成果。合成数据集的平均分类精度为84.24％，图像数据集的平均分类精度达到89.08％。在半监督分类中取得了积极的成果。合成数据集的平均分类精度为84.24％，图像数据集的平均分类精度达到89.08％。