Unified one-step multi-view spectral clustering
Multi-view spectral clustering, which exploits the complementary information among graphs
of diverse views to obtain superior clustering results, has attracted intensive attention …
of diverse views to obtain superior clustering results, has attracted intensive attention …
Structured graph learning for scalable subspace clustering: From single view to multiview
Graph-based subspace clustering methods have exhibited promising performance.
However, they still suffer some of these drawbacks: they encounter the expensive time …
However, they still suffer some of these drawbacks: they encounter the expensive time …
Efficient and effective one-step multiview clustering
Multiview clustering algorithms have attracted intensive attention and achieved superior
performance in various fields recently. Despite the great success of multiview clustering …
performance in various fields recently. Despite the great success of multiview clustering …
Fast multi-view clustering via prototype graph
Multi-view clustering attracts considerable attention due to its effectiveness in unsupervised
learning. However, previous multi-view spectral clustering methods include two separated …
learning. However, previous multi-view spectral clustering methods include two separated …
User clustering and optimized power allocation for D2D communications at mmWave underlaying MIMO-NOMA cellular networks
S Solaiman, L Nassef, E Fadel - IEEE Access, 2021 - ieeexplore.ieee.org
Fifth-generation (5G) cellular networks are being developed to meet the ever-growing data
traffic across mobile devices and their applications. The core of 5G cellular networks is …
traffic across mobile devices and their applications. The core of 5G cellular networks is …
Local-global fuzzy clustering with anchor graph
Recently, anchor-based strategy is getting a lot of attention, which extends spectral
clustering to reveal the dual relation between samples and features. However, the …
clustering to reveal the dual relation between samples and features. However, the …
Fuzzy K-means clustering with discriminative embedding
Fuzzy K-Means (FKM) clustering is of great importance for analyzing unlabeled data. FKM
algorithms assign each data point to multiple clusters with some degree of certainty …
algorithms assign each data point to multiple clusters with some degree of certainty …
Fusion of centroid-based clustering with graph clustering: An expectation-maximization-based hybrid clustering
Z Uykan - IEEE Transactions on Neural Networks and Learning …, 2021 - ieeexplore.ieee.org
This article extends the expectation-maximization (EM) formulation for the Gaussian mixture
model (GMM) with a novel weighted dissimilarity loss. This extension results in the fusion of …
model (GMM) with a novel weighted dissimilarity loss. This extension results in the fusion of …
Fast self-supervised clustering with anchor graph
Benefit from avoiding the utilization of labeled samples, which are usually insufficient in the
real world, unsupervised learning has been regarded as a speedy and powerful strategy on …
real world, unsupervised learning has been regarded as a speedy and powerful strategy on …
Discrete and balanced spectral clustering with scalability
Spectral Clustering (SC) has been the main subject of intensive research due to its
remarkable clustering performance. Despite its successes, most existing SC methods suffer …
remarkable clustering performance. Despite its successes, most existing SC methods suffer …