Robust Orthogonal NMF with Label Propagation for Image Clustering
This work addresses noise robustness and supervised integration in image clustering, but it appears incremental as it builds on existing NMF frameworks with added regularization and constraints.
The paper tackles the problem of noise sensitivity and limited supervised information in non-negative matrix factorization (NMF) for image clustering by proposing a robust orthogonal NMF with label propagation, which outperforms state-of-the-art NMF methods on eight public image datasets.
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.