提出一种抗噪强的图像聚类方法，融合标签传播提升精度
Robust Orthogonal NMF with Label Propagation for Image Clustering
- 引入非凸损失与正交约束，增强对噪声的鲁棒性
- 在8个公开数据集上优于主流NMF方法，准确率显著提升
- 适合噪声干扰大、标注信息少的图像聚类任务
非负矩阵分解（NMF）是图像聚类中广泛应用的无监督学习方法。然而，在真实场景中，现有NMF方法对噪声敏感，且难以有效利用少量监督信息。为此，本文提出统一的非凸框架——鲁棒正交非负矩阵分解（RONMF），将图拉普拉斯和标签传播作为正则项，并采用更有效的非凸结构度量重构误差，同时对基矩阵施加正交约束以降低噪声影响，从而实现更高鲁棒性。为求解RONMF，设计基于交替方向乘子法（ADMM）的优化算法，所有子问题均有闭式解，保证计算效率。在八个公开图像数据集上的实验表明，所提RONMF在多种标准指标下均优于当前最优NMF方法，展现出优异的鲁棒性。代码将发布于https://github.com/slinda-liu。
原文摘要 · Abstract (English)
Non-negative matrix factorization (NMF) is a popular unsupervised learning approach widely used in image clustering. However, in real-world clustering scenarios, most existing NMF methods are highly sensitive to noise corruption and are unable to effectively leverage limited supervised information. To overcome these drawbacks, we propose a unified non-convex framework with label propagation called robust orthogonal nonnegative matrix factorization (RONMF). This method not only considers the graph Laplacian and label propagation as regularization terms but also introduces a more effective non-convex structure to measure the reconstruction error and imposes orthogonal constraints on the basis matrix to reduce the noise corruption, thereby achieving higher robustness. To solve RONMF, we develop an alternating direction method of multipliers (ADMM)-based optimization algorithm. In particular, all subproblems have closed-form solutions, which ensures its efficiency. Experimental evaluations on eight public image datasets demonstrate that the proposed RONMF outperforms state-of-the-art NMF methods across various standard metrics and shows excellent robustness. The code will be available at https://github.com/slinda-liu.
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