Self-Supervised Clustering based on Manifold Learning and Graph Convolutional Networks

Author(s)

Lopes, Leonardo Tadeu ; Guimaraes Pedronette, Daniel Carlos ; IEEE

• Document type: Journal article
• Source: 2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV); v. N/A, p. 10-pg., 2023-01-01.

Abstract

In spite of the huge advances in supervised learning, the common requirement for extensive labeled datasets represents a severe bottleneck. In this scenario, other learning paradigms capable of addressing the challenge associated with the scarcity of labeled data represent a relevant alternative solution. This paper presents a novel clustering method called Self-Supervised Graph Convolutional Clustering (SGCC)1, which aims to exploit the strengths of different learning paradigms, combining unsupervised, semi-supervised, and self-supervised perspectives. An unsupervised manifold learning algorithm based on hypergraphs and ranking information is used to provide more effective and global similarity information. The hypergraph structures allow identifying representative items for each cluster, which are used to derive a set of small but highconfident clusters. Such clusters are taken as soft-labels for training a Graph Convolutional Network (GCN) in a semi-supervised classification task. Once trained in a selfsupervised setting, the GCN is used to predict the cluster of remaining items. The proposed SGCC method was evaluated both in image and citation networks datasets and compared with classic and recent clustering methods, obtaining high-effective results in all scenarios. (AU)

• FAPESP's process: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks
• Grantee: Daniel Carlos Guimarães Pedronette
• Support Opportunities: Research Grants - Young Investigators Grants - Phase 2
• FAPESP's process: 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert
• Grantee: João Paulo Papa
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE