no code implementations • CVPR 2025 • HaiMing Xu, Qianqian Wang, Boyue Wang, Quanxue Gao
Multi-view clustering is effective in unsupervised multi-view data analysis and has received considerable attention.
no code implementations • CVPR 2025 • Bowen Zhao, Qianqian Wang, Zhengming Ding, Quanxue Gao
Although some methods have been proposed to address the issue of missing node attributes, they come with the following limitations: i) Existing methods are often not tailored specifically for clustering tasks and struggle to address missing attributes effectively.
no code implementations • 24 Sep 2024 • Fangfang Li, Quanxue Gao, Cheng Deng, Wei Xia
However, it combines the K-means clustering and dimensionality reduction processes for optimization, leading to limitations in the clustering effect due to the introduced hyperparameters and the initialization of clustering centers.
no code implementations • 7 Apr 2024 • Yu Lei, Guoshuai Sheng, Fangfang Li, Quanxue Gao, Cheng Deng, Qin Li
However, current attention-based models may overlook the transferability of visual features and the distinctiveness of attribute localization when learning regional features in images.
no code implementations • 7 Apr 2024 • Yichen Bao, Han Lu, Quanxue Gao
Fuzzy K-Means clustering is a critical technique in unsupervised data analysis.
no code implementations • 1 Apr 2024 • Rui Wang, Jing Li, Quanxue Gao, Cheng Deng
Nevertheless, existing multi-view clustering methods based on anchor graph factorization lack adequate cluster interpretability for the decomposed matrix and often overlook the inter-view information.
no code implementations • 3 Mar 2024 • Wenhui Zhao, Quanxue Gao, Guangfei Li, Cheng Deng, Ming Yang
Despite their successes, current methods lack interpretability in the clustering process and do not sufficiently consider the complementary information across different views.
no code implementations • 26 Feb 2024 • Jing Li, Quanxue Gao, Qianqian Wang, Cheng Deng, Deyan Xie
Multi-view clustering method based on anchor graph has been widely concerned due to its high efficiency and effectiveness.
no code implementations • 24 Feb 2024 • Shikun Mei, Fangfang Li, Quanxue Gao, Ming Yang
Additionally, we evolve the concept of the membership matrix between cluster centers and samples in FKM into an anchor graph encompassing multiple anchor points and samples.
no code implementations • 12 May 2023 • Quanxue Gao, Qianqian Wang, Han Lu, Wei Xia, Xinbo Gao
Although numerous clustering algorithms have been developed, many existing methods still leverage k-means technique to detect clusters of data points.
no code implementations • 29 Mar 2023 • Jing Li, Quanxue Gao, Qianqian Wang, Wei Xia, Xinbo Gao
Multi-view clustering (MVC) based on non-negative matrix factorization (NMF) and its variants have received a huge amount of attention in recent years due to their advantages in clustering interpretability.
no code implementations • 15 Oct 2021 • Wei Xia, Quanxue Gao, Ming Yang, Xinbo Gao
Thus, for the OOS nodes, SCAGC can directly calculate their clustering labels.
no code implementations • 15 Aug 2021 • Quanxue Gao, Wei Xia, Xinbo Gao, Xiangdong Zhang, Qin Li, DaCheng Tao
Despite the impressive clustering performance and efficiency in characterizing both the relationship between data and cluster structure, existing graph-based multi-view clustering methods still have the following drawbacks.
no code implementations • 29 Jun 2021 • Tianyu Jiang, Quanxue Gao, Xinbo Gao
Specifically, we construct a hidden and tractable large graph by anchor graph for each view and well exploit complementary information embedded in anchor graphs of different views by tensor Schatten p-norm regularizer.
no code implementations • 29 Mar 2020 • Qianqian Wang, Zhengming Ding, Zhiqiang Tao, Quanxue Gao, Yun Fu
Nowadays, with the rapid development of data collection sources and feature extraction methods, multi-view data are getting easy to obtain and have received increasing research attention in recent years, among which, multi-view clustering (MVC) forms a mainstream research direction and is widely used in data analysis.