计算机科学 ›› 2020, Vol. 47 ›› Issue (7): 71-77.doi: 10.11896/jsjkx.200200106
李向利1,2,3, 贾梦雪1,4
LI Xiang-li1,2,3, JIA Meng-xue1,4
摘要： 随着多媒体技术的发展,信息越来越多的以图片的形式出现。如何对海量的无标签图片进行聚类,是机器学习领域的热点问题。而图像聚类在人脸识别、手写数字识别等领域也有着重要的作用。由于图片数据通常以非负矩阵的形式存储,因此非负矩阵分解算法(NMF)在图像聚类领域得到了广泛的应用。但是NMF算法直接在数据的原始空间进行处理,这就导致NMF算法所得的图片标签易受到数据采集过程中含有的噪声等不利因素的影响。为了解决这些问题,提出了一种基于预处理的超图非负矩阵分解算法(Nonnegative Matrix Factorization with Hypergraph Based on Per-treatments,PHGNMF)。PHGNMF算法将预处理操作和超图的思想引入到NMF算法。在预处理的过程中,使用灰度处理来去除图片中不同光线条件所带来的影响,采用小波分析来提取图片的低时频子图,同时降低了算法所处理的矩阵维度。采取构建超图的方法来进一步保留对聚类结果有重要影响的数据局部结构。最后在5个主流数据集上的实验验证了PHGNMF算法相对于传统算法的有效性,结果显示聚类精度提升了2%～7%,标准互信息在部分数据集上提升了2%～5%。
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