Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

In this paper, we propose an online clustering method called Contrastive Clustering (CC)
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …

In this paper, we study how to make unsupervised cross-modal hashing (CMH) benefit from
contrastive learning (CL) by overcoming two challenges. To be exact, i) to address the …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

Subspace clustering is an effective method that has been successfully applied to many
applications. Here, we propose a novel subspace clustering model for multi-view data using …

Deep clustering learns deep feature representations that favor clustering task using neural
networks. Some pioneering work proposes to simultaneously learn embedded features and …

Deep clustering utilizes deep neural networks to learn feature representation that is suitable
for clustering tasks. Though demonstrating promising performance in various applications …

Existing subspace clustering methods typically employ shallow models to estimate
underlying subspaces of unlabeled data points and cluster them into corresponding groups …

Recently, some contrastive learning methods have been proposed to simultaneously learn
representations and clustering assignments, achieving significant improvements. However …