Contrastive clustering

Y Li, P Hu, Z Liu, D Peng, JT Zhou… - Proceedings of the AAAI …, 2021 - ojs.aaai.org
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 …

Weakly supervised low-rank representation for hyperspectral anomaly detection

W Xie, X Zhang, Y Li, J Lei, J Li… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In this article, we propose a weakly supervised low-rank representation (WSLRR) method for
hyperspectral anomaly detection (HAD), which formulates deep learning-based HAD into a …

Unsupervised discriminative feature learning via finding a clustering-friendly embedding space

W Cao, Z Zhang, C Liu, R Li, Q Jiao, Z Yu, HS Wong - Pattern Recognition, 2022 - Elsevier
In this paper, we propose an enhanced deep clustering network (EDCN), which is
composed of a Feature Extractor, a Conditional Generator, a Discriminator and a Siamese …

Diversity embedding deep matrix factorization for multi-view clustering

Z Chen, P Lin, Z Chen, D Ye, S Wang - Information Sciences, 2022 - Elsevier
Multi-view clustering has attracted increasing attention by reason of its ability to leverage the
complementarity of multi-view data. Existing multi-view clustering methods have explored …

Twin contrastive learning for online clustering

Y Li, M Yang, D Peng, T Li, J Huang, X Peng - International Journal of …, 2022 - Springer
This paper proposes to perform online clustering by conducting twin contrastive learning
(TCL) at the instance and cluster level. Specifically, we find that when the data is projected …

Positive-incentive noise

X Li - IEEE Transactions on Neural Networks and Learning …, 2022 - ieeexplore.ieee.org
Noise is conventionally viewed as a severe problem in diverse fields, eg, engineering and
learning systems. However, this brief aims to investigate whether the conventional …

Embedding graph auto-encoder for graph clustering

H Zhang, P Li, R Zhang, X Li - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
Graph clustering, aiming to partition nodes of a graph into various groups via an
unsupervised approach, is an attractive topic in recent years. To improve the representative …

Sparse and flexible projections for unsupervised feature selection

R Wang, C Zhang, J Bian, Z Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In recent decades, unsupervised feature selection methods have become increasingly
popular. Nevertheless, most of the existing unsupervised feature selection methods suffer …

Neural network model-based control for manipulator: An autoencoder perspective

Z Li, S Li - IEEE Transactions on Neural Networks and …, 2021 - ieeexplore.ieee.org
Recently, neural network model-based control has received wide interests in kinematics
control of manipulators. To enhance learning ability of neural network models, the …

Deep spectral clustering with regularized linear embedding for hyperspectral image clustering

Y Zhao, X Li - IEEE Transactions on Geoscience and Remote …, 2023 - ieeexplore.ieee.org
The past decade has witnessed the rapid development of deep learning techniques,
especially for large-scale and complex datasets. However, it is still a noteworthy problem in …