Results 11 to 20 of about 155,627 (235)

Kernel-Based Intuitionistic Fuzzy Clustering Image Segmentation Based on Grey Wolf Optimizer With Differential Mutation

IEEE Access, 2021
Conventional fuzzy clustering algorithms present several disadvantages with respect to image segmentation, including a tendency to arrive at local optima and a relatively high sensitivity to noise and initial cluster centers.
Xiangxiao Lei, Honglin Ouyang
doaj   +1 more source

Efficient Multiple Kernel k-Means Clustering With Late Fusion

IEEE Access, 2019
The recently proposed multiple-kernel clustering algorithms have demonstrated promising performance in various applications. However, most of the existing methods suffer from high computational complexity and intensive time cost.
Siwei Wang, En Zhu, Jingtao Hu, Miaomiao Li, Kaikai Zhao, Ning Hu, Xinwang Liu   +6 more
doaj   +1 more source

Single-Cell RNA-Sequencing Data Clustering via Locality Preserving Kernel Matrix Alignment

IEEE Access, 2020
Single-cell RNA-sequencing (scRNA-seq) data provide opportunities to reveal new insights into many biological problems such as elucidating cell types. An effective approach to elucidate cell types in complex tissues is to partition the cells into several
Xiao Zheng, Jiajia Chen, Chang Tang, Suqin Zhou   +3 more
doaj   +1 more source

Consensus Multiple Kernel K-Means Clustering With Late Fusion Alignment and Matrix-Induced Regularization

IEEE Access, 2019
Multiple kernel clustering (MKC) attracts considerable attention due to its competitive performance in unsupervised learning. However, we observe that most of the existing MKC approaches do not sufficiently consider the correlation between different ...
Jingtao Hu, Miaomiao Li, En Zhu, Siwei Wang, Xinwang Liu, Yongping Zhai   +5 more
doaj   +1 more source

Multi-Omics Data Fusion via a Joint Kernel Learning Model for Cancer Subtype Discovery and Essential Gene Identification

Frontiers in Genetics, 2021
The multiple sources of cancer determine its multiple causes, and the same cancer can be composed of many different subtypes. Identification of cancer subtypes is a key part of personalized cancer treatment and provides an important reference for ...
Jie Feng, Limin Jiang, Shuhao Li, Jijun Tang, Jijun Tang, Jijun Tang, Lan Wen   +6 more
doaj   +1 more source

Influence of kernel clustering on an RBFN

CAAI Transactions on Intelligence Technology, 2019
Classical radial basis function network (RBFN) is widely used to process the non-linear separable data sets with the introduction of activation functions.
Changming Zhu, Duoqian Miao, Duoqian Miao   +2 more
doaj   +1 more source

Robust Multi-View Subspace Clustering Via Weighted Multi-Kernel Learning and Co-Regularization

IEEE Access, 2020
Using multi-kernel learning to deal with the non-linear relationship of data has become a new research topic in the field of multi-view subspace clustering.
Yilu Zheng, Xiaoqian Zhang, Yinlong Xu, Mingwei Qin, Zhenwen Ren, Xuqian Xue   +5 more
doaj   +1 more source

Penalized Clustering of Large Scale Functional Data with Multiple Covariates [PDF]

, 2008
In this article, we propose a penalized clustering method for large scale data with multiple covariates through a functional data approach. In the proposed method, responses and covariates are linked together through nonparametric multivariate functions (
Ma, Ping, Zhong, Wenxuan
core   +4 more sources

Kernel Truncated Regression Representation for Robust Subspace Clustering [PDF]

, 2020
Subspace clustering aims to group data points into multiple clusters of which each corresponds to one subspace. Most existing subspace clustering approaches assume that input data lie on linear subspaces.
Peng, Dezhong, Wang, Wei, Yao, Xin, Zhen, Liangli   +3 more
core   +2 more sources

Robust Kernelized Multiview Clustering Based on High-Order Similarity Learning

IEEE Access, 2022
This paper explores the robust kernelized multi-view clustering (MVC) for nonlinear data. The existing MVC methods aim to excavate the complementary and consensus information from multi-view data lies in the linear space for clustering.
Yanying Mei, Zhenwen Ren, Bin Wu, Tao Yang, Yanhua Shao   +4 more
doaj   +1 more source