One-hot constrained symmetric nonnegative matrix factorization for image clustering

Abstract

Semi-supervised Symmetric Non-Negative Matrix Factorization (SNMF) has proven to be an effective clustering method. However, most existing semi-supervised SNMF approaches rely on sophisticated techniques to incorporate supervised information, which results in increased hyper-parameter tuning and model complexity. To achieve better performance with lower complexity, we propose a novel semi-supervised SNMF method called One-hot Constrained SNMF (OCSNMF). This method introduces a parameter-free embedding strategy for partial label information, representing the clustering assignments of labeled data points as one-hot vectors in the SNMF decomposition. We present an iterative algorithm to solve the optimization problem of the proposed OCSNMF, along with analyses of convergence and complexity. Experimental results on six image datasets demonstrate the superiority of OCSNMF compared to several state-of-the-art methods. The code can be obtained from: https://github.com/ljisxz/OCSNMF.