Contrastive clustering
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 …
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …
Weakly supervised low-rank representation for hyperspectral anomaly detection
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 …
hyperspectral anomaly detection (HAD), which formulates deep learning-based HAD into a …
Unsupervised discriminative feature learning via finding a clustering-friendly embedding space
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 …
composed of a Feature Extractor, a Conditional Generator, a Discriminator and a Siamese …
Diversity embedding deep matrix factorization for multi-view clustering
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 …
complementarity of multi-view data. Existing multi-view clustering methods have explored …
Twin contrastive learning for online clustering
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 …
(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 …
learning systems. However, this brief aims to investigate whether the conventional …
Embedding graph auto-encoder for graph clustering
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 …
unsupervised approach, is an attractive topic in recent years. To improve the representative …
Sparse and flexible projections for unsupervised feature selection
In recent decades, unsupervised feature selection methods have become increasingly
popular. Nevertheless, most of the existing unsupervised feature selection methods suffer …
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 …
control of manipulators. To enhance learning ability of neural network models, the …
Deep spectral clustering with regularized linear embedding for hyperspectral image clustering
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 …
especially for large-scale and complex datasets. However, it is still a noteworthy problem in …