Results 231 to 240 of about 156,082 (279)

Locally adaptive multiple kernel clustering

Neurocomputing, 2014
Abstract Conventional multiple kernel learning aims to construct a global combination of multiple kernels in input space. For a data set which has varying local distributions in input space, using a uniform combination of multiple kernels may not always work well.
Lujiang Zhang, Xiaohui Hu
exaly   +2 more sources

Knowledge-Induced Multiple Kernel Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
The introduction of domain knowledge opens new horizons to fuzzy clustering. Then knowledge-driven and data-driven fuzzy clustering methods come into being. To address the challenges of inadequate extraction mechanism and imperfect fusion mode in such class of methods, we propose the Knowledge-induced Multiple Kernel Fuzzy Clustering (KMKFC) algorithm.
Yiming Tang, Zhifu Pan, Xianghui Hu
exaly   +4 more sources

Subspace segmentation-based robust multiple kernel clustering

Information Fusion, 2020
Abstract Multiple kernel clustering (MKC) is an important research topic during the last few decades. It optimally combines a group of pre-specified base kernels to improve clustering performance. Though demonstrating promising performance in various applications, this task is still challenging due to lack of reliable discriminative guidance for the ...
Sihang Zhou, En Zhu, Xinwang Liu
exaly   +2 more sources

Incomplete Multiple Kernel Alignment Maximization for Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple kernel alignment (MKA) maximization criterion has been widely applied into multiple kernel clustering (MKC) and many variants have been recently developed. Though demonstrating superior clustering performance in various applications, it is observed that none of them can effectively handle incomplete MKC, where parts or all of the pre-specified
Xinwang Liu
exaly   +3 more sources

Hierarchical Multiple Kernel Clustering

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
Current multiple kernel clustering algorithms compute a partition with the consensus kernel or graph learned from the pre-specified ones, while the emerging late fusion methods firstly construct multiple partitions from each kernel separately, and then obtain a consensus one with them.
Jiyuan Liu 0003, Xinwang Liu 0002, Siwei Wang 0001, Sihang Zhou 0001, Yuexiang Yang   +4 more
openaire   +1 more source

Multiple kernel clustering with corrupted kernels

Neurocomputing, 2017
Abstract Multiple kernel clustering (MKC) algorithms usually learn an optimal kernel from a group of pre-specified base kernels to improve the clustering performance. However, we observe that existing MKC algorithms do not well handle the situation that kernels are corrupted with noise and outliers.
Teng Li 0010, Yong Dou, Xinwang Liu 0002, Yang Zhao 0003, Qi Lv   +4 more
openaire   +1 more source

Optimal Neighborhood Multiple Kernel Clustering with Adaptive Local Kernels

IEEE Transactions on Knowledge and Data Engineering, 2021
Multiple kernel clustering (MKC) algorithm aims to group data into different categories by optimally integrating information from a group of pre-specified kernels. Though demonstrating superiorities in various applications, we observe that existing MKC algorithms usually do not sufficiently consider the local density around individual data samples and ...
Jiyuan Liu 0003, Xinwang Liu 0002, Jian Xiong 0002, Qing Liao 0001, Sihang Zhou 0001, Siwei Wang 0001, Yuexiang Yang   +6 more
openaire   +1 more source

Fuzzy clustering with Multiple Kernels

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011
In this paper, the kernel fuzzy c-means clustering algorithm is extended to an adaptive cluster model which maps data points to a high dimensional feature space through an optimal convex combination of homogenous kernels with respect to each cluster.
Naouel Baili, Hichem Frigui
openaire   +1 more source

Optimal Neighborhood Kernel Clustering with Multiple Kernels

Proceedings of the AAAI Conference on Artificial Intelligence, 2017
Multiple kernel $k$-means (MKKM) aims to improve clustering performance by learning an optimal kernel, which is usually assumed to be a linear combination of a group of pre-specified base kernels. However, we observe that this assumption could: i) cause limited kernel representation capability; and ii) not sufficiently consider the ...
Xinwang Liu 0002, Sihang Zhou 0001, Yueqing Wang, Miaomiao Li 0001, Yong Dou, En Zhu, Jianping Yin   +6 more
openaire   +1 more source

Mutual structure learning for multiple kernel clustering

Information Sciences, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhenglai Li, Chang Tang, Xiao Zheng, Zhiguo Wan, Kun Sun 0002, Wei Zhang 0049, Xinzhong Zhu   +6 more
openaire   +2 more sources