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Article
Computer Science, Artificial Intelligence
Chang Tang et al.
Summary: Multi-view spectral clustering integrates graph information and embedding matrices from different views to obtain discrete clustering labels with a one-step strategy, solving the issues of information loss and performance degradation in existing methods. The proposed method's effectiveness is verified through a set of experiments.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Chang Tang et al.
Summary: Unsupervised feature selection aims to select discriminative features from high-dimensional data without using data labels. This paper proposes a novel method called MGF(2)WL, which uses multiple graph fusion and feature weight learning to address the issues of poor quality similarity graph and ineffective feature reconstruction/projection model.
SCIENCE CHINA-INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Zhenjiao Liu et al.
Summary: This article presents a novel algorithm called IMC-NLT for incomplete multi-view clustering. By utilizing non-negative matrix factorization and a low-rank tensor, IMC-NLT effectively extracts hidden information from incomplete views, overcoming limitations of existing algorithms. Experimental results demonstrate that IMC-NLT outperforms baseline methods and produces stable and promising results.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Guangyan Ji et al.
Summary: Incomplete multi-view clustering methods have been widely studied in order to handle incomplete multi-view data. However, most existing methods only consider balanced incomplete multi-view data, where the missing rate is the same for each view. In reality, the missing rate for each view is often different, resulting in unbalanced incomplete multi-view data. In this paper, we propose an innovative method, UIMVC/LTGL, based on low-rank tensor graph learning, to address unbalanced incomplete multi-view data. Experimental results on seven datasets demonstrate the superiority of our proposed method.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Wei Xia et al.
Summary: Incomplete multiview clustering is a challenging problem in unsupervised learning. Existing methods only consider intraview similarity structure and neglect interview similarity structure. This study proposes a model based on tensor completion and connectivity constraint to leverage both complementary information and spatial structure for clustering.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Information Systems
Wentao Rong et al.
Summary: The proposed integrative multi-view subspace clustering method learns a consensus affinity by merging subspace representations of different views on a Grassmann manifold, enhancing clustering performance while preserving geometric structures. The method outperforms state-of-the-art approaches on synthetic and real-world datasets, extracting highly representative and latent common information for improved clustering performance.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Deyan Xie et al.
Summary: This paper proposes a method for multi-view spectral clustering based on weighted tensor nuclear norm minimization, which distinguishes singular values and learns high-order transition probability matrices to explore information embedded in multi-view data. An efficient optimization algorithm is designed. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods.
Article
Computer Science, Hardware & Architecture
Ao Li et al.
Summary: A reliable similarity learning scheme is proposed for multiview clustering on uncertain data, improving robustness by enhancing the quality and structure of graphs, exploring high-order correlations among view graphs, and integrating data recovery, view-specific graphs, and latent consensus tensor structure into a unified framework for joint optimization.
IEEE TRANSACTIONS ON RELIABILITY
(2021)
Article
Computer Science, Information Systems
Juncheng Lv et al.
Summary: In this paper, a method of fusing multi-view information in a partition space to enhance the robustness of multi-view clustering is proposed. By generating multiple partitions and integrating them to find a shared partition, the model unifies graph learning, generation of basic partitions, and view weight learning for better quality outputs.
INFORMATION SCIENCES
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Xinwang Liu et al.
Summary: This paper proposes a novel MKC algorithm with local kernel alignment, which aligns the similarity of a sample to its k-nearest neighbors with the ideal similarity matrix. By focusing on closer sample pairs that should stay together and avoiding involving unreliable similarity evaluation for farther sample pairs, the proposed algorithm consistently outperforms the state-of-the-art ones, verifying the effectiveness of the local kernel alignment criterion.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Article
Automation & Control Systems
Ping Li et al.
IEEE TRANSACTIONS ON CYBERNETICS
(2020)
Article
Computer Science, Artificial Intelligence
Zhao Kang et al.
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Automation & Control Systems
Jie Wen et al.
IEEE TRANSACTIONS ON CYBERNETICS
(2020)
Article
Chemistry, Analytical
Pei Zhang et al.
Article
Computer Science, Artificial Intelligence
Mahdi Abavisani et al.
INFORMATION FUSION
(2018)
Article
Computer Science, Artificial Intelligence
Shuhang Gu et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2017)
Article
Computer Science, Artificial Intelligence
Jing Zhao et al.
INFORMATION FUSION
(2017)
Article
Mathematics, Applied
Misha E. Kilmer et al.
SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS
(2013)
Article
Mathematics, Applied
Misha E. Kilmer et al.
LINEAR ALGEBRA AND ITS APPLICATIONS
(2011)
