Research Area:  Machine Learning

Abstract:

As a leading graph clustering technique, spectral clustering is one of the most widely used clustering methods that captures complex clusters in data. However, some of its deficiencies, such as the high computational complexity in eigen decomposition and the guidance without supervised information, limit its real applications. To get rid of the deficiencies, we propose a self-supervised spectral clustering algorithm. In this algorithm, we define an exemplar constraint which reflects the relations between objects and exemplars. We provide the related analysis to show that it is more suitable for unsupervised learning. Based on the exemplar constraint, we build an optimization model for self-supervised spectral clustering so that we can simultaneously learn clustering results and exemplar constraints. Furthermore, we propose an iterative method to solve the new optimization problem. Compared to other existing versions of spectral clustering algorithms, the new algorithm can use the low computational costs to discover a high-quality cluster structure of a data set without prior information. Furthermore, we did a number of experiments of algorithm comparison and parameter analysis on benchmark data sets to illustrate that the proposed algorithm is very effective and efficient.

Keywords:  
Clustering technique
Computational complexity
Eigen decomposition
Supervised information
Unsupervised learning

Author(s) Name:  Liang Bai, Yunxiao Zhao, Jiye Liang

Publisher name:  Elsevier

DOI:  10.1016/j.patcog.2022.108975

Volume Information:  Volume 132

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0031320322004551