Latent Space Learning-Based Ensemble Clustering

Abstract

Ensemble clustering fuses a set of base clusterings and shows promising capability in achieving more robust and better clustering results. The existing methods usually realize ensemble clustering by adopting a co-association matrix to measure how many times two data points are categorized into the same cluster based on the base clusterings. Though great progress has been achieved, the obtained co-association matrix is constructed based on the combination of different connective matrices or its variants. These methods ignore exploring the inherent latent space shared by multiple connective matrices and learning the corresponding co-association matrices according to this latent space. Moreover, these methods neglect to learn discriminative connective matrices, explore the high-order relation among these connective matrices and consider the latent space in a unified framework. In this paper, we propose a Latent spacE leArning baseD Ensemble Clustering (LEADEC), which introduces the latent space shared by different connective matrices and learns the corresponding connective matrices according to this latent space. Specifically, we factorize the original multiple connective matrices into a consensus latent space representation and the specific connective matrices. Meanwhile, the orthogonal constraint is imposed to make the latent space representation more discriminative. In addition, we collect the obtained connective matrices based on the latent space into a tensor with three orders to investigate the high-order relations among these connective matrices. The connective matrices learning, the high-order relation investigation among connective matrices and the latent space representation learning are integrated into a unified framework. Experiments on seven benchmark datasets confirm the superiority of LEADEC compared with the existing representive methods.