A sampling-based density peaks clustering algorithm for large-scale data
S Ding, C Li, X Xu, L Ding, J Zhang, L Guo, T Shi - Pattern Recognition, 2023 - Elsevier
With the rapid development of information technology, massive amount of data is generated.
How to discover useful information to support decision-making has become one of the …
How to discover useful information to support decision-making has become one of the …
Unsupervised deep clustering via contractive feature representation and focal loss
Deep clustering aims to promote clustering tasks by combining deep learning and clustering
together to learn the clustering-oriented representation, and many approaches have shown …
together to learn the clustering-oriented representation, and many approaches have shown …
Fast subspace clustering by learning projective block diagonal representation
Abstract Block Diagonal Representation (BDR) has attracted massive attention in subspace
clustering, yet the high computational cost limits its widespread application. To address this …
clustering, yet the high computational cost limits its widespread application. To address this …
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 …
Contrastive self-representation learning for data clustering
This paper is concerned with self-representation subspace learning. It is one of the most
representative subspace techniques, which has attracted considerable attention for …
representative subspace techniques, which has attracted considerable attention for …
Dual-graph regularized concept factorization for multi-view clustering
Matrix factorization is an important technology that obtains the latent representation of data
by mining the potential structure of data. As two popular matrix factorization techniques …
by mining the potential structure of data. As two popular matrix factorization techniques …
Entropy regularization for unsupervised clustering with adaptive neighbors
Graph-based clustering has been considered as an effective kind of method in unsupervised
manner to partition various items into several groups, such as Spectral Clustering (SC) …
manner to partition various items into several groups, such as Spectral Clustering (SC) …
Robust semi-supervised multi-view graph learning with sharable and individual structure
The construction of a high-quality multi-view consensus graph is key to graph-based semi-
supervised multi-view learning (GSSMvL) methods. However, most existing GSSMvL …
supervised multi-view learning (GSSMvL) methods. However, most existing GSSMvL …
Deep subspace image clustering network with self-expression and self-supervision
C Chen, H Lu, H Wei, X Geng - Applied Intelligence, 2023 - Springer
The subspace clustering algorithms for image datasets apply a self-expression coefficient
matrix to obtain the correlation between samples and then perform clustering. However …
matrix to obtain the correlation between samples and then perform clustering. However …