Semi-supervised non-negative matrix factorization with structure preserving for image clustering

The code for paper '' Semi-supervised non-negative matrix factorization with structure preserving for image clustering''. This paper constructs a new label matrix with weights and further construct a label constraint regularizer to both utilize the label information and maintain the intrinsic structure of NMF. Based on the label constraint regularizer, the basis images of labeled data are extracted for monitoring and modifying the basis images learning of all data by establishing a basis regularizer. By incorporating the label constraint regularizer and the basis regularizer into NMF, a new semi-supervised NMF method is introduced. The proposed method is applied to image clustering and experimental results demonstrate the effectiveness of the proposed method in contrast with state-of-the-art unsupervised and semi-supervised algorithms.本代码对应论文《面向图像聚类的半监督保结构非负矩阵分解》。该论文构建了一种带权重的新型标签矩阵，并进一步构造标签约束正则项，以同时利用标签信息并保留非负矩阵分解（Non-negative Matrix Factorization, NMF）的内在结构。基于该标签约束正则项，研究人员提取带标签数据的基图像，通过构建基正则项来监控并修正全量数据的基图像学习过程。将标签约束正则项与基正则项融入非负矩阵分解框架后，本文提出了一种新型半监督非负矩阵分解方法。所提方法被应用于图像聚类任务，实验结果表明，相较于当前最优的无监督与半监督算法，该方法的有效性得到了充分验证。