Self-supervised semi-supervised nonnegative matrix factorization for data clustering. (May 2023) - Chavoshinejad, Jovan; Seyedi, Seyed Amjad; Akhlaghian Tab, Fardin; Salahian, Navid
Highlights: A self-supervised semi-supervised NMF method is proposed for data clustering. We define an effective semi-supervised NMF model with attractive and repulsive interactions. An ensemble of semi-supervised NMFs is designed to model a self-supervised one. The self-supervised model generates pseudo supervisory signals to boost semi-supervised learning. Experimental results demonstrate the effectiveness of the proposed method in semi-supervised clustering tasks. Abstract: Semi-supervised nonnegative matrix factorization exploits the strengths of matrix factorization in successfully learning part-based representation and is also able to achieve high learning performance when facing a scarcity of labeled data and a large amount of unlabeled data. Its major challenge lies in how to learn more discriminative representations from limited labeled data. Furthermore, self-supervised learning has been proven very effective at learning representations from unlabeled data in various learning tasks. Recent research works focus on utilizing the capacity of self-supervised learning to enhance semi-supervised learning. In this paper, we design an effective Self-Supervised Semi-Supervised Nonnegative Matrix Factorization (S 4 NMF) in a semi-supervised clustering setting. The S 4 NMF directly extracts a consensus result from ensembled NMFs with similarity and dissimilarity regularizations. In an iterative process, this self-supervisory information will be fed back to the proposed model to boost semi-supervised learning and form more distinct clusters. The proposed iterative algorithm is used to solve the given problem, which is defined as an optimization problem with a well-formulated objective function. In addition, the theoretical and empirical analyses investigate the convergence of the proposed optimization algorithm. To demonstrate the effectiveness of the proposed model in semi-supervised clustering, we conduct extensive experiments on standard benchmark datasets. The source code for reproducing our results can be found at https://github.com/ChavoshiNejad/S4NMF .