Self-supervised semi-supervised nonnegative matrix factorization for data clustering. (May 2023)

• Record Type: Journal Article
• Authors: Chavoshinejad, Jovan; Seyedi, Seyed Amjad; Akhlaghian Tab, Fardin; Salahian, Navid
• Is Part Of: Pattern recognition. Volume 137(2023)
• Publication Date: 2023-05
• Subjects: Nonnegative matrix factorization -- Semi-supervised learning -- Self-supervised learning -- Ensemble clustering; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.109282
• Languages: English
• ISSNs: 0031-3203

Abstract

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 .