Scalable Multiple Kernel k-means Clustering
With its simplicity and effectiveness, k-means is immensely popular, but it cannot perform
well on complex nonlinear datasets. Multiple kernel k-means (MKKM) demonstrates the …
well on complex nonlinear datasets. Multiple kernel k-means (MKKM) demonstrates the …
Self-Weighted Euler -means Clustering
Clustering is used widely in various kinds of signal processing tasks, in which-means is
warmly welcomed by the researchers due to its efficiency and simplicity. Nevertheless, it fails …
warmly welcomed by the researchers due to its efficiency and simplicity. Nevertheless, it fails …
Heat Kernel Diffusion for Enhanced Late Fusion Multi-view Clustering
G Yang, J Zou, Y Chen, L Du… - IEEE Signal Processing …, 2024 - ieeexplore.ieee.org
Recent advancements in Multi-view Clustering (MVC) have highlighted the benefits of late
fusion techniques. However, existing late fusion-based MVC (LFMVC) approaches often …
fusion techniques. However, existing late fusion-based MVC (LFMVC) approaches often …
One-Step Late Fusion Multi-View Clustering with Compressed Subspace
Late fusion multi-view clustering (LFMVC) has become a rapidly growing class of methods in
the multi-view clustering (MVC) field, owing to its excellent computational speed and …
the multi-view clustering (MVC) field, owing to its excellent computational speed and …
Self-Paced and Discrete Multiple Kernel k-Means
Multiple Kernel K-means (MKKM) uses various kernels from different sources to improve
clustering performance. However, most of the existing models are non-convex, which is …
clustering performance. However, most of the existing models are non-convex, which is …
A unified framework for discrete multi-kernel k-means with Kernel diversity regularization
Multiple kernel clustering seeks to combine several kernels for boosting the clustering
performance. However, most existing MKKM methods fail to evaluate kernel correlation …
performance. However, most existing MKKM methods fail to evaluate kernel correlation …
Distributed Multi-kernel Learning Based on Gaussian Mixture Model with Missing Data
S Chen, Y Liu - Proceedings of the 2nd International Conference on …, 2023 - dl.acm.org
Distributed classification, as an important distributed learning task, has received much
attention for more than two decades. However, it is still a challenge to achieve acceptable …
attention for more than two decades. However, it is still a challenge to achieve acceptable …